Benjamin Paris

Julien Eynard

Stéphane Grieu

Thierry Talbert
email: talbert@univ-perp.fr

Monique Polit
email: polit@univ-perp.fr

Heating control schemes for energy management in buildings

Keywords: multi-energy buildings, energy management strategy, heating, PID, Model Predictive Control (MPC), Fuzzy Logic Control (FLC)

because of both the global energy crisis and the necessary improvement of energy efficiency in buildings, one of the largest sectors of energy consumption and greenhouse gases emissions, a strategy allowing managing energy resources is proposed. Its aim is reducing energy consumption and promoting the use of renewable energy, while ensuring thermal comfort, when heating "multi-energy" buildings, thanks to indoor temperature control schemes. Three schemes (based on a commonly-used PID controller and on the combination of PID and model predictive or fuzzy controllers) were tested in simulation, using dynamic models describing the thermal behavior of a building, and fully met the management strategy's requirements, especially reducing the consumption of fossil energy. Three criteria describing the way energy is used and controlled in real-time were defined with the aim of evaluating the control schemes performance and adapting the strategy to the specific use of a building. The PID-MPC provided the best results while the PID-FLC proved to be a very good compromise, thanks to both the flexibility and the adaptability offered by fuzzy logic, between the easy-todevelop but not-very-efficient PID and the efficient but hard-to-develop PID-MPC.

Introduction

The European energy context reveals that the building, industry and transport sectors are the three largest sectors of energy consumption. As a consequence, the Directive "Energy Performance of Building" (EPBD) [1], focusing on energy use in buildings and urging the members of the European Union (EU) of improving energy efficiency, has been adopted. In France, where 25% of greenhouse gases emissions and 46% of energy consumption [START_REF]French Environment and Energy Management Agency, Les chiffres clés du bâtiment en 2006 (Building sector key figures in 2006)[END_REF] are due to buildings, both the "Thermal Regulation 2005" [START_REF][END_REF] and the "Energy Performance Diagnosis" [4] are in agreement with the just-mentioned Directive's recommendation and are more and more rigorous about energy performance. Usually, the building's Energy Performance Indicator (EPI) is calculated to classify buildings [START_REF][END_REF]4]. Unfortunately, this global indicator expresses the amount of energy consumption only, without any explanation. It does not dissociate the various energy consumption components and does not explain how energy is consumed in buildings. As a consequence, proposing efficient control approaches (as well as new evaluation criteria) allowing ensuring thermal comfort, while reducing significantly energy consumption, has become mandatory.

Taking a look at the state of the art about energy in buildings [START_REF] Dounis | Advanced control systems engineering and comfort management in building environment-A review[END_REF], one can highlight the way this theme is handled by researchers. Mathews et al. (2008) [START_REF] Mathews | Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort[END_REF], [START_REF] Levermore | Building energy management systems: an application to heating and control[END_REF] [START_REF] Levermore | Building energy management systems: an application to heating and control[END_REF], [START_REF] Bernard | Optimal building energy management. Part II: Control[END_REF] [START_REF] Bernard | Optimal building energy management. Part II: Control[END_REF], [START_REF] Kolokotsa | Implementation of an integrated indoor environment and energy management system[END_REF] [START_REF] Kolokotsa | Implementation of an integrated indoor environment and energy management system[END_REF], Ben-Nakhi et al. (2001) [START_REF] Ben-Nakhi | Energy conservation in buildings through efficient A/C control using neural networks[END_REF], [START_REF] Kalogirou | Artificial neural networks for the prediction of the energy consumption of a passive solar building[END_REF] [START_REF] Kalogirou | Artificial neural networks for the prediction of the energy consumption of a passive solar building[END_REF] and [START_REF] Gonzalez | Prediction of hourly energy consumption in buildings based on a feedback artificial neural network[END_REF] [START_REF] Gonzalez | Prediction of hourly energy consumption in buildings based on a feedback artificial neural network[END_REF] presented works related to energy management, cost strategies and energy consumption forecast, while [START_REF] Chen | Fuzzy adaptive networks in thermal comfort[END_REF] [START_REF] Chen | Fuzzy adaptive networks in thermal comfort[END_REF], [START_REF] Calvino | The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller[END_REF] [START_REF] Calvino | The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller[END_REF], [START_REF] Kummert | Optimal heating control in a passive solar commercial building[END_REF] [START_REF] Kummert | Optimal heating control in a passive solar commercial building[END_REF], [START_REF] Morel | Neurobat, a predictive and adaptive heating control system using artificial neural networks[END_REF] [START_REF] Morel | Neurobat, a predictive and adaptive heating control system using artificial neural networks[END_REF], [START_REF] Nygard | Predictive thermal control of building systems[END_REF] [START_REF] Nygard | Predictive thermal control of building systems[END_REF], Lute et al. (2000) [START_REF] Lute | Optimal indoor temperature control using a predictor[END_REF], [START_REF] Liang | Thermal comfort control based on neural network for HVAC application[END_REF] [START_REF] Liang | Thermal comfort control based on neural network for HVAC application[END_REF] and [START_REF] Argiriou | Use of artificial neural networks for predicting the heating requirements of single family houses[END_REF] [START_REF] Argiriou | Use of artificial neural networks for predicting the heating requirements of single family houses[END_REF] focus on fuzzy, neural network, optimal or predictive control of thermal conditions in buildings. However, energy management in buildings is not really correlated with energy savings and clear strategies to optimize the use of several (fossil and renewable) energy resources are not defined. Moreover, these works are often devoted to the specific use of a given building. That is why the present work deals with an efficient and widelyadaptable strategy allowing managing fossil and renewable energy resources in buildings (one speaks of "multi-energy" buildings). Indoor temperature control schemes were developed with the aim of both favoring energy savings and increasing the renewable energy contribution, while ensuring thermal comfort. Because of its significant impact on energy consumption, heating is the key-point. As a consequence, advanced indoor temperature control schemes allowing managing (fossil and renewable) energy resources are proposed. Moreover, and because PID controllers are commonly used in buildings engineering, the advanced control schemes proposed are built on the basis of a PID controller (let us note that the performance of this controller was considered as "reference" performance). This allows implementing these schemes in buildings even if a control system based on such a controller is already in use and improving its performance. First, a PID-MPC (Model Predictive Control) control scheme is proposed, as the combination of PID and MPC controllers. Model predictive controllers can inherently handle constraints. Such controllers are model-based and discrete controllers allowing calculating an optimal command sequence and anticipating set-point changes. Moreover, and with the aim of easily taking into account expert knowledge about thermal comfort and energy management in multi-energy buildings, a PID-FLC (Fuzzy Logic Control) control scheme is also proposed, as the combination of PID and fuzzy controllers. Let us note that a building mock-up has been built, instrumented and modeled to test the proposed control schemes. Instrumentation consists of height temperature sensors and two resistors used as heat sources. Finally, criteria (about fossil energy consumption, thermal comfort and control performance) describing the way energy is used and controlled in real-time were defined with the aim of evaluating both the energy management strategy proposed and the control schemes (PID, PID-MPC and PID-FLC).

Energy management strategy

Strategy fundamentals

Reviewing both the "Thermal Regulation 2005" and the "Energy Performance Diagnosis" while taking into consideration the main objective of the French government, dealing with a significant reduction of fossil energy consumption in buildings, allowed defining the fundamentals of the proposed strategy. First, one can define various energy consumption components, such as heating and cooling, water heating, lighting, cooking, ventilation or electronics. One can highlight, and this is a key-point, that the majority of energy consumed is for heating (largely dependent on outside temperatures), which account for about 60% of all delivered energy consumed in buildings. As a consequence, the proposed strategy focuses on managing heating systems, considering multi-energy buildings. On another hand, the just-mentioned documentation indicates, first, the way an energy performance diagnosis can be carried out (via the characterization of the building materials used, analyzing thermal insulation properties or investigating the influence on energy consumption of the building's localization) and, secondly, recommends considering buildings through a systemic approach (one speaks of multi-area buildings). Moreover, it highlight the use of a building (offices, dwellings, public buildings…) as one of the key points when trying to evaluate the energy impact of buildings. Consequently, we want the proposed strategy to be easily and widely adaptable to the specific use of a building. As most of the thermal regulations used in the world to classify buildings from their energy performance, the French "Thermal Regulation 2005" is based on the building's Energy Performance Indicator (kWh.m -2 .year -1 ). As previously mentioned, this global indicator expresses the amount of energy consumption only, without explaining how energy is consumed. So, as a part of the proposed strategy, new and useful criteria, dealing with thermal comfort, fossil energy consumption and global performance, were defined (section 2.2). Although renewable energy supply is increasing continuously, supply systems are often controlled in an inadequate way, or taking no account of all the available energy resources. Ensuring thermal comfort is also essential because of its psychological implications. In some cases, people may refuse to live or to work in a particular environment. That is why the proposed strategy deals with optimizing energy performance and promoting renewable energy, while ensuring thermal comfort, in multi-energy buildings (with both fossil and renewable energy resources), using indoor temperature controllers.

Criteria for evaluating the control schemes performance

Tools are needed for comparing the controllers' performance. As previously mentioned, the building's EPI only provides partial information. That is why additional criteria, (criterion to be minimized) and both comfort ( ) and performance ( ) criteria (criteria to be maximized), were developed. These criteria incorporate the way energy is in real-time used and controlled. Let us note that common criteria for human feeling in heated rooms or buildings such as both "Predictive Mean Vote" and "Percentage of Dissatisfied" criteria [START_REF]International Standard, ISO 7730, Moderate thermal environment-Determination PMV and PPD indices and specifications of the conditions for thermal comfort[END_REF], are found in the literature but do not match with the objectives. First, the criterion is defined as the percentage of the fossil energy consumed compared with the total energy used. Then, the comfort criterion , based on temperature set-point tracking, specifies the mean relative error between (°C) and (°C). Finally, the criterion focuses on both the performance of the proposed controller, comparing the two justmentioned criteria, and the way the control scheme impacts energy consumption (Equation 1). Because both the use and occupancy of a building impact on temperature set-point profiles, specific temperature instructions (for offices and residential buildings respectively), recommended by the French "Thermal Regulation 2005" [22], were used. This allows testing in several ways the robustness of the proposed control schemes. The lengths and the values of the temperature setpoints are related to the building use (scholar, hospital…). For example, an office building has 3 temperature set-points ( ), 19°C, 16°C and 7°C, according to the hour of the day and the day of the week. Consequently, both the amplitude, ranging between 7°C and 22°C, and the frequency of temperature variations are weak. Restarting the regulation process after a break was also considered.

Simulation dynamic models

Dynamic models are necessary to test the proposed control schemes. The first dynamic model developed is a theoretical model, based on the thermal diffusivity equation that can be expressed as a temperature function. A second dynamic model describes the thermal behavior of a building mock-up. In both models, two heat sources are considered: the main source uses renewable energy, while the secondary source uses fossil energy.

Theoretical model

The theoretical model [START_REF] Paris | Gestion des ressources énergétique d'un bâtiment : contrôle flou (Fuzzy management of energy resources in buildings)[END_REF] was developed using Equation 2 and describes how temperature evolves in a building room. To simplify the model, and axes are supposed to be infinite while the room is supposed to be constituted of homogenous and isotropic material. So, Equation 2 becomes Equation 3:

The three right-hand terms of Equation 3 depict the influence of conduction, convection and heat sources respectively. Two warmers are considered. To solve Equation 3, many explicit and implicit methods were tested, but the Crank-Nicholson discrimination method was preferred. Finally, Equation 4 focuses on the influence of external conditions, with the difference between outdoor temperature and the walls temperature. Using the theoretical dynamic model, preliminary results about heat resources management and indoor temperature control were promising [START_REF] Paris | A prototype for on line monitoring and control of energy performance for renewable energy buildings[END_REF]. That is why another dynamic model, describing the thermal behavior of an instrumented building mock-up, was developed to test the control schemes in conditions we want close to real conditions.

Mock-up model

Having the possibility to instrument real buildings with a set of sensors and to test temperature controllers is not easy. That is why a building mock-up was built and instrumented [START_REF] Paris | Travaux pratiques sur la gestion optimale d'un bâtiment EnR (Practical work about optimal management of renewableenergy buildings)[END_REF]. Working with a mock-up gives flexibility concerning both sensors and heat sources localization. The lack of thermal inertia favors reactivity and avoids energy waste; a small amount of electricity is consumed for heating the mock-up. Its design is related to its scale, the building materials used and the possibility of easily quantifying the thermal losses. Instrumentation consists of eight temperature sensors (one outdoor sensor and seven indoor sensors) and two resistors used as heat sources. A monitoring system is used for collecting and treating data. Temperature and heat power datasets being needed to model the building mock-up, several tests were carried out according to various both the power of the two resistors and time periods. Figure 1 presents an example of temperatures acquisition during a period of about thirty days (from September 9, 2008 to October 6, 2008), with a sampling time of 60s.

Figure 1. Mock-up temperatures acquisition.

The study of the mock-up thermal behavior leads to the model which structure is described by Equation 5. Using an iterative process of error minimization depicted by Equation 6, all the parameters of Equation 5 For each temperature, the fit (a similarity criterion) between measured and modeled data is computed using Equation 7. A mean similarity higher than 90% is obtained when carrying out the identification process. All the parameters of the mock-up model are listed by Table 1.

Table 1. Parameters of the mock-up model.

The seven equations obtained are then used in simulation for estimating the mock-up average indoor temperature and testing the proposed control schemes when applying the proposed energy management strategy. About disturbances, only outdoor temperature is taken into account.

Indoor temperature control schemes

Simulations were carried out for both testing the management strategy proposed (focusing on heating) and evaluating the performance of the indoor temperature control schemes developed, using the two just-mentioned models and a unique set of outdoor temperatures. The evaluation criteria (section 2.2) based on fossil and renewable energy consumption and on temperature setpoint tracking were computed. Let's remember that both developed models incorporate two heat sources, a renewable energy warmer (the main heat source) and a fossil energy warmer (the secondary heat source). Respective powers are 700W (

) and 300W ( ) for the theoretical model and 80W (

) and 34W ( ) for the mock-up model. Moreover, and with the aim of being in agreement with common behaviors, in any case, the renewable energy warmer is used until power saturation is reached. At this point, the fossil energy warmer starts working. and are the power of and respectively, while is the time index (Equation 8).

As a consequence, the renewable resource is firstly used. In addition, and this is the standard case, all the controllers' parameters are optimized to maximize the performance criterion . In the same way, various constrains and aditional parameters (they will be explained in the next sections) were considered when tuning the advanced control schemes (based on PID and MPC or FLC controllers), as required for taking into account the specific use of a building.

PID control scheme

A standard PID controller is a control mechanism [START_REF] Luyben | Essentials of Process Control[END_REF], widely used in buildings engineering for heating control [START_REF] Dounis | Advanced control systems engineering and comfort management in building environment-A review[END_REF][START_REF] Mathews | Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort[END_REF][START_REF] Levermore | Building energy management systems: an application to heating and control[END_REF][START_REF] Bernard | Optimal building energy management. Part II: Control[END_REF][START_REF] Kolokotsa | Implementation of an integrated indoor environment and energy management system[END_REF]. That is why it was considered as the reference controller and was the basic component of the proposed advanced control schemes (PID-MPC and PID-FLC schemes). Let us remind its structure (Figure 2) for discrete time control with anti-windup considerations (Equation 9). Using , the controller computes (the "unsaturated" heat power).

(the "saturated" heat power) then both (the power of ) and (the power of ) are deduced from . Outdoor temperature impacts on indoor temperature and is as a consequence considered as a disturbance. The anti-windup system was realized using a standard method. It controls the PID integral action and preserves both the stability and the performance of the closed-loop.

PID-MPC control scheme

A model predictive controller is a model-based and discrete controller allowing calculating an optimal command sequence. To elaborate this sequence, one needs both a linear model and a working point of the system to be controlled [START_REF] García | Model predictive control: Theory and practice -A survey[END_REF][START_REF] Qin | A survey of industrial model predictive control technology[END_REF]. Future set-point knowledge and external disturbances forecast allow anticipating set-point changes and taking into account the influence of these disturbances. The MPC controller used estimates the way outdoor temperature is evolving on a prediction horizon and computes optimal increments on a command horizon (shorter than

). So, a new optimization is carried out for each time step. The values of the two horizons are the following ones, for the theoretical and mock-up models, respectively:

= 60 minutes and = 30 minutes, = 10 minutes and = 5 minutes. The advanced heating control scheme proposed, depicted by Figure 3, is defined according to the following guidelines: the PID controller (the basic component of the control scheme proposed) estimates the power of ( ), while the MPC controller, using the forecasted outdoor temperature , the temperature set-point and the mean temperature inside the considered building , specifies the power of ( ) and if the power of needs to be adjusted ( ).

Figure 3. Framework of the PID-MPC control scheme.

Being a model-based controller is one of the main drawbacks of the MPC controller, jointly to its very extensive on-line computational effort, but, in the other hand, it can inherently handle constraints [START_REF] García | Model predictive control: Theory and practice -A survey[END_REF][START_REF] Qin | A survey of industrial model predictive control technology[END_REF]. Equation 11 describes the objective function, based on indoor temperature set-point tracking and fossil energy consumption, used for calculating the optimal sequences about heat power ( and ) and how the constraints are defined, according to both the considered model (Equation 3 or Equation 5) and the PID controller equations (Equation 9). As a key-point, let us specify that a weight has been assigned to with the aim of impacting significantly on the fossil energy consumption. Section 5.1 focuses on the impact of this weight on the evaluation criteria proposed (section 2.2) and the way one can use it to take into account the specific use of a multi-energy building when implementing the management strategy proposed.

with the following constraints: , and , .

PID controller

Heating system 12). Let us remember that according to the specific use of a multi-energy building or because the way energy resources are managed has changed, one can also choose the value of with the aim of minimizing the consumption of fossil energy or favoring thermal comfort. with: .

PID-FLC control scheme

Both the structure of the control scheme and the heating control strategy remain the same as when using model predictive and PID controllers (Figure 4). Let us just remember that the concept of fuzzy sets deals with the representation of classes whose boundaries are not quite determined. Fuzzy sets can be defined by exemplification, ranking elements according to their typicality with respect to the concept underlying the sets. They preserve a gradual and smooth transition from one category into another and avoid abrupt discontinuities [START_REF] Zadeh | Fuzzy sets[END_REF]. Their ability and possibility theory to model gradual properties or soft constraints whose satisfaction is a matter of degree, as well as information pervaded with imprecision and uncertainly, makes them useful in a great variety of applications [START_REF] Huang | The application of fuzzy controller for fuel cell generating studies[END_REF][START_REF] Traoré | Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant[END_REF][START_REF] Traoré | Control of sludge height in a secondary settler using fuzzy algorithms[END_REF][START_REF] Wakabayashi | Fuzzy control of a nylon polymerization semi-batch reactor[END_REF][START_REF] Van Der Wal | Application of fuzzy logic control in industry[END_REF][START_REF] Verbruggen | Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems?[END_REF]. That is why the PID-FLC scheme is proposed for easily taking into account the specific use of a building, thanks to the design of appropriate fuzzy rules [START_REF] Bouslama | Fuzzy control rules and their natural control laws[END_REF]. From the difference between the set-point temperature ( ) and the indoor mean temperature ( ), the PID controller estimates the power of ( ) while a 1 st fuzzy module determines if this power needs to be corrected (

). From and , a 2 nd fuzzy module evaluates the power of ( ).

Figure 4. Framework of the PID-FLC control scheme.

PID controller Heating system

Disturbance

Fuzzy module FLCRE Fuzzy module FLCFE

The values of and are normalized between -1 and +1 ( ) and 0 and + 1 ( ) respectively, then denormalized using the gains and . One needs, first, to characterize all the above-mentioned parameters and their respective "universes of discourse" using fuzzy sets and membership functions and, secondly, to design appropriate fuzzy rules (section 5.2.1.) that map inputs to outputs, with the aim of optimizing the performance criterion (thanks to the fuzzification process and the design of the fuzzy rules, one can easily take into account the specific use of a multi-energy building, favoring thermal comfort or minimizing the fossil energy consumption). Equation 13describes the way this criterion can be maximized, jointly optimizing all the controllers' gains ( , , , and ), according to the system model.

with: , , , and

Results and discussion

This section focuses on the results obtained when applying the proposed strategy, allowing managing energy resources in a multi-energy building, using the mock-up model. Because the results are similar when using the theoretical model, they will not be presented. The impact on the evaluation criteria ( , and

) of the weight assigned to (PID-MPC control scheme), the fuzzification phase, and the design of the fuzzy rules (PID-FLC control scheme) was examined with the aim of answering to the following question: how indoor temperature controllers can be adapted to the specific use of a building? Finally, the results obtained when using the PID-MPC and PID-FLC control schemes are compared to the reference results (provided by the PID scheme).

Impact on the evaluation criteria of the weight ω assigned to (PID-MPC scheme)

To answer to the just-mentioned question, one needs, first, to understand how the proposed criteria are evolving, considering the weight assigned to and designed to limit the use of fossil energy (Equation 14). Table 2 and Figures 5 and6 depict, according to both the temperature set-points chosen to deal with offices and houses respectively and a weight ranging between 0.0001 and 10, the respective values of , and . Table 2 also deals with renewable and fossil energy consumptions. Taking a look at the just-mentioned table and figures, one can first note that, whatever the use of the building, a weak favors comfort ( ) while a strong ω promotes fossil energy savings ( ): in other words, the higher is the weight assigned to ( → 0), limiting the use of renewable energy, the lower are the criteria and . However, the use of a building determines which is the optimal value of : for example, a bad comfort criteria is not suitable for hospitals while a scholar building can promote energy savings during holidays. So, whatever the set-point (for offices or houses), any weights can be assigned to according to both the control specific requirements and the specific use of a multi-energy building. One can notice that, for offices, = 0.05 is the best compromise between comfort and fossil energy use, the performance criterion being maximal ( ). For houses, = 0.03 leads to . Let us also note that, whatever the use of the building, = 0.02 allows maximizing the comfort criterion ( ; ).

Figure 5. Impact of the weight on the evaluation criteria (offices). Figure 6. Impact of the weight on the evaluation criteria (houses).

Taking as a reference the performance of the PID scheme, one can observe that the PID-MPC scheme, considering offices ( is set to 0.05), allows reducing the percentage of the fossil energy consumed compared with the total energy used by 24%, from 6.50% to 4.94%, and increasing both the comfort and performance criteria by 2.2%, from 72.03% to 73.63%, and 4.8%, from 65.53% to 68.70%, respectively. Similar results are obtained for houses ( is set to 0.03): the PID-MPC scheme allows reducing by 29.3%, from 9.62% to 6.80%, and increasing both criteria and by 15.2%, from 61.86% to 71.26%, and 7%, from 60.24% to 64.45%, respectively. Even though some of these percentages seem to be weak, they are significant due to the average life of buildings.

Taking another look at Table 2 and again taking as a reference the performance of the PID control scheme, one can also note that, whatever the set-point, the PID-MPC control scheme allows reducing significantly the fossil energy consumption while the renewable energy consumption remains stable or decreases slightly. For offices ( 5.2. Impact on the evaluation criteria and on energy consumption of the fuzzification phase and both the number and the design of fuzzy rules (PID-FLC scheme)

Universes of discourse

Let us remember the structure of the PID-FLC scheme (Figure 4): from the difference between the set-point temperature and the indoor mean temperature ( ), the PID controller estimates the power of ( ) while a first fuzzy module determines if this power needs to be corrected ( ). From both ε and , a second fuzzy module evaluates the power of ( ). As previously mentioned, one needs, first, to characterize the fuzzy modules' input and output parameters and their "universes of discourse" using fuzzy sets, triangular or trapezoidal-shaped membership functions (the membership functions are chosen to be bell-shaped with a minimum and a maximum equal to 0 and 1 respectively) and linguistic labels and, secondly, to design appropriate fuzzy rules, with the aim of maximizing the performance criterion (let us remember that this is the "standard" case but the design of the fuzzy rules can be adapted with the aim of maximizing the comfort criterion or minimizing the percentage of the fossil energy consumed compared with the total energy used ). Because of the thermal inertia, heat transfers between adjacent thermal zones and the heating system dimensioning, indoor temperatures in buildings may be in the range 0°C-30°C. Moreover, and according to the "Thermal regulation 2005", temperature setpoints ( ) may be in the range 7°C-22°C. As a result, values for the difference between the setpoint temperature and the (current) mean indoor temperature range between -24°C and +24°C (

). As previously mentioned, the values of and are normalized between -1 and +1 and 0 and + 1 respectively then denormalized using the gains and . So, and . Finally, being saturated at 80W, the universe of discourse of is defined as follows:

. To be concise and because, whatever the set-point, similar results are obtained, only results for offices will be presented in the following sections of the paper.

Optimal configuration allowing maximizing the performance criterion

Let us note that, because of measurement error due to the data acquisition tool used, control accuracy is ± 0.5°C. Usually, one considers that energy consumption increases by 7% over a year if the regulated indoor temperature rises above the set-point by 1°C. Both factors were considered during the fuzzification phase of the parameters. Let us also note that the control tool was designed without any consideration about control speed which is, however, a significant criterion in control engineering. Nevertheless, and with the aim of avoiding both the saturation of the renewable energy resource and set-point overflow, a progressive relaunch of the control process was promoted. Table 3 summarizes all the significant results, obtained when using the PID-FLC control scheme for implementing the proposed energy management strategy, according to the fuzzification of the modules' input and output parameters and both the number and the design of the fuzzy rules. Nine configurations are proposed (from A to I). Table 3 highlights how the justmentioned considerations are related with both the previously-defined criteria , and and (fossil and renewable) energy consumption, when implementing the proposed control scheme in an office while taking into account its use and specific constraints. Increasing, from a starting configuration (A), the number of both the fuzzy sets (common triangular or trapezoidal membership functions and linguistic labels were associated to the sets) used to split the respective universes of discourse of the modules' input and output parameters and the fuzzy rules led to the optimal configuration (E) which maximizes the performance criterion ( ). Let us also note, first, that the shape of the triangular membership functions used for characterizing around zero was adjusted (the length of their respective bases was reduced) from configuration B (this configuration allows minimizing ) to configuration C (this configuration leads to very good performance, rather close to the performance of the optimal configuration E, but using less fuzzy rules) then from configuration D to configuration E (in this case, the number of fuzzy sets has also increased from 5 to 7) (Figure 7), secondly, that the design of the fuzzy rules was modified from configuration E to configuration F then from configuration F to configuration G and, finally, that the shape of the triangular membership functions used for characterizing was adjusted (the length of their respective bases was extended) from configuration H to configuration I. Finally, taking a look at configurations A to I, one can first highlight the two following key-points: (1) the difference between the set-point temperature and the current temperature (used as fuzzy modules input) has to be characterized by splitting its chosen universe of discourse into enough fuzzy sets, notably around zero, to obtain a good comfort criterion ( ) and to avoid oscillations of the controlled temperature around the set-point; (2) splitting the universe of discourse of into enough fuzzy sets allows limiting the use of fossil energy and improves consequently the criterion . Both key-points impact the performance criterion ( ) and define the way the PID-FLC control scheme can be implemented according to the use of a building. Taking again as a reference the performance of the PID scheme, one can observe that the PID-FLC scheme (when considering the optimal configuration E), allows reducing the percentage of the fossil energy consumed compared with the total energy used by 11.7%, from 6.50% to 5.74%, while increasing both the comfort and the performance criteria by 0.5%, from 72.03% to 72.38%, and 1.7%, from 65.53% to 66.64%, respectively. Looking at Table 3, one can also note that configuration E provides the highest comfort criterion among all of the configurations we studied and that fossil energy consumption is significantly reduced while renewable energy consumption increases moderately:

is reduced by 9.7%, from 521.02 Wh.m -2 to 470.66 Wh.m -2 , while is increased by 3.2%, from 7494.29 Wh.m -2 to 7731.35 Wh.m -2 . Similar results are obtained for houses.

Design of new rules for improving the control quickness

As just-highlighted, considering the PID-FLC scheme, the way the fuzzy rules are designed, related not only to the proposed control strategy but also to the fuzzification of the fuzzy modules' input and output parameters, directly impacts both the evaluation criteria and the closed-loop performance. Let us talk about some highlights from the proposed fuzzy rules for module (configuration E) (Table 4). First, the rule "IF ε is NM THEN is NH" (rule #2) was designed with the aim of both making faster the control and limiting the power of : when it is too hot, heating has to be significantly reduced. About the rule "IF ε is PM THEN is PL" (rule #6), it was designed to limit the power of during heating times: when it is too cold, heating as to be slightly increased. With the aim of giving flexibility to the proposed control strategy and thanks to some specific adjustments of the two just-mentioned rules (rules #2 and #6), one can try to improve the control quickness. The first way (this leads to configuration E') is designing the rule #6 as follows: "IF ε is PM THEN is PH" (when it is too cold, heating has to be significantly increased). The second way (this leads to configuration E") is designing the rule #2 as follows: "IF ε is NM THEN is NL" (when it is too hot, heating has to be slightly reduced). While the control quickness is improved, these two adjustments also lead to increasing the criterion and decreasing both the comfort and performance criteria and (Figure 12). In all cases, the design of the fuzzy rules reflects a compromise between thermal comfort and energy consumption, taking into account a given situation and the specific use of a building. Finally, let us note that, to avoid incoherent behaviors and to be in agreement with the proposed strategy, some potential rules were obviously not taken into account, such as the following ones: "IF ε is NL THEN is PM" (when it is slightly too hot, heating has to be significantly increased) or "IF ε is PL THEN is NL" (when it is slightly too cold, heating has to be slightly reduced).

Figure 12. New configurations for improving the control quickness (offices).

Synthesis and temperature set-point tracking

The present section deals with the synthesis of the results obtained, using both the mock-up model and one of the control schemes developed (PID, PID-MPC and PID-FLC), when applying the proposed energy management strategy, focusing on heating and indoor temperature, to multienergy buildings. Let us remember that, whatever the temperature set-point (for offices or houses), similar results are obtained. That why only results about offices will be presented again. Table 6 summarizes the respective performance of the control schemes proposed, taking into consideration the fossil and renewable energy consumed and the respective values of , and . As a synthesis, one can first highlight that, concerning energy consumption, savings of fossil energy are about 26.9% when using the PID-MPC scheme and about 9.8% when using the PID-FLC scheme. At the same time, renewable energy consumption slightly decreases with the PID-MPC scheme (about 2%) while it increases by 3.2% with the PID-FLC scheme. Whitout any doubt, and whatever the parameter considered, the PID scheme obtains the worst results over the other considered control schemes. The PID-MPC control scheme is the best performer, mainly because of a low consumption of fossil energy i.e. because of the lowest and the highest comfort criterion ( ), leading to the best performance criterion ( ), about 3% higher than when using the PID-FLC control scheme. However, developing and testing a PID-MPC controller is harder and longer than developing and testing a classical PID controller and, as previously mentioned, requires an accurate model of the building, not always easy to obtain. Furthermore, implementing this kind of advanced controllers needs an embedded numerical optimizer and a fast microprocessor. As an example, Figure 13 depicts the temperature set-point tracking using the three proposed schemes. Whatever the scheme, this tracking can be considered as satisfactory. However, taking a look at the figure, one can observe specific behaviors (notably when outdoor temperature decreases) as a result of a compromise between set-point tracking and energy consumption and related to both the design of the fuzzy rules (PID-FLC scheme) and the way the PID-MPC's objective function (including a weight assigned to ) is formulated.

Conclusion

Taking into consideration the European energy context and according to the latest laws meeting EU requirements about energy certification schemes for buildings, the present paper focuses on investigating ways for improving the energy performance of buildings. That is why an efficient and widely-adaptable strategy, based on the specific use of a building and allowing managing both fossil and renewable energy resources in multi-energy buildings, is proposed. Indoor temperature control schemes were developed with the aim of favoring energy savings and increasing the renewable energy contribution, while ensuring thermal comfort. Because of its significant impact on energy consumption, heating is the key-point (it account for about 60% of all delivered energy consumed in buildings). Moreover, and because PID controllers are commonly used in buildings engineering, the advanced control schemes proposed (both PID-MPC and PID-FLC schemes) are built on the basis of a PID controller. This allows implementing these schemes in buildings even if a control system based on such a controller is already in use and improving its performance. A MPC (Model Predictive Controller) is a versatile and powerful model-based and discrete controller allowing calculating an optimal command sequence while a FLC (Fuzzy Logic Controller) provides a remarkably simple way to draw definite conclusions from vague, imprecise or missing information, incorporating a linguistic rule-based approach.

Because both the use and occupancy of a building impact on temperature set-point profiles, specific temperature instructions (for offices and residential buildings respectively), recommended by the French "Thermal Regulation 2005", were used. Dynamic models being necessary to test the proposed control schemes, a theoretical model, based on the thermal diffusivity equation that can be expressed as a temperature function, was first developed. A second dynamic model describes the thermal behavior of an instrumented building mock-up. In both models, two heat sources are considered: the main source uses renewable energy, while the secondary source uses fossil energy. Finally, and because the building's Energy Performance Indicator (commonly used to classify buildings) does not dissociate the various energy consumption components and does not explain how energy is consumed in buildings, criteria (about fossil energy consumption, thermal comfort and control performance) describing the way energy is used and controlled in real-time were defined with the aim of evaluating both the energy management strategy and the indoor temperature control schemes proposed.

Despite some limitations due to specific features, the commonly-used PID scheme provided pretty good simulation results. However, its performance is the worst among the three tested control schemes. The results that best meet the proposed strategy objectives are provided by the PID-MPC scheme, as a result of both the lowest and the highest comfort criterion ( ), consequently leading to the best performance criterion ( ). When implementing the PID-MPC scheme, the weight assigned to the power of the renewable energy warmer impacts significantly on the fossil energy consumption. However, and because the MPC controller is a model-based controller which on-line computational effort is extensive, developing and implementing such a controller is a quite hard task. The PID-FLC scheme also provided better results than the PID scheme. Thanks to the flexibility offered by both the fuzzification of the fuzzy modules' input and output parameters and the design of the fuzzy rules, the PID-FLC scheme allows favoring one of the evaluation criteria defined and adapting to the specific use of a building. As a consequence, it can easily affect in one way or another the behavior of the controlled system and, consequently, of the two heat sources. So, whatever the control scheme used, and taking into consideration the specific use of a building, one can promote the set-point tracking performance (i.e. the comfort criterion) while increasing the consumption of fossil energy ( ) or promote energy savings while decreasing comfort. Promoting is a compromise.

As a conclusion, and taking a look at the dynamic of the controlled system, one can highlight that the PID-MPC scheme anticipates set-point changes and allows, consequently, a better management of the fossil and renewable energy resources, limiting both the number of shutdown and restart sequences and the saturation of the main heat source. Finally, while the best global performance remains PID-MPC's, the PID-FLC is a good compromise between the easy-to-develop but not-very-efficient PID and the efficient but hard-to-develop PID-MPC. Because the three control schemes proposed were only developed in simulation, they need now to be tested using more complex models and/or situations, with the aim of being finally implemented in real buildings.

Figure

  Figure 2. Framework of the PID control scheme.

  Testing several values of , one can find the right value allowing maximizing the performance criterion (Equation

Figure 7 .

 7 Figure 7. Remarkable configurations (offices).

Figures 8 , 9 ,

 89 10 and 11 present the respective fuzzifications of , , and while Tables 4 and 5 depict the two sets of fuzzy rules (modules FLCFE and FLCRE) characterizing configuration E. The following (usual) linguistic labels were associated to the fuzzy sets: NH (Negative High), NM (Negative Medium), NL (Negative Low), AZ (Approximately Zero), PL (Positive Low), PM (Positive Medium) and PH (Positive High) for , and for , NH (Negative High), NL (Negative Low), AZ (Approximately Zero), PL (Positive Low) and PH (Positive High) for and, finally, null, weak, medium, strong and full for .

Figure 8 .

 8 Figure 8. Fuzzification of (optimal configuration E).

Figure 9 .

 9 Figure 9. Fuzzification of (optimal configuration E).

Figure 10 .

 10 Figure 10. Fuzzification of (optimal configuration E).

Figure 11 .

 11 Figure 11. Fuzzification of

Figure 13 .

 13 Figure 13. Temperature set-point tracking using the PID, PID-MPC and PID-FLC control schemes (offices).

  are identified for each of the seven temperature sensors (

			Indoor temperature measured by the i th sensor		°C
			Outdoor temperature			°C
			Experimental temperature data		°C
			Modeled temperature data		°C
			Power of the first heat source		W
			Power of the second heat source		W
			Inertia of temperature			-
			Influence of the first heat source on temperature		-
			Influence of the second heat source on temperature	-
			Influence of outdoor temperature on temperature		-
			Time index (	and	60s)		-
			Error criterion			-
			Sample number			-
							):
	with:	,	,	,	,	and	.

  2. Framework of the PID control scheme.

	With the aim of optimizing the performance criterion , Equation 10 allows finding the most
	accurate coefficients of the PID controller.
	with:	,	and	.
				Disturbance
			PID controller	Heating system
			Building's indoor mean temperature	°C
			Temperature set-point	°C
			Proportional gain	-
				Integral gain	-
				Derivative gain	-
				Sampling time	s
			Anti-windup time constant	s
				Integration state	-
				Derivation state	-
			Unsaturated heat power (computed by the PID controller)	W
			Saturated heat power (computed by the PID controller)	W
			Maximum heat power	W

Table 2 .

 2 Respective values of the evaluation criteria and energy consumption according to the weight ω assigned to (offices and houses).

			Offices		Houses
	ω/PID
	PID	7494.29	521.02	6.50 72.03 65.53	7414.05	786.73	9.62 61.86 60.24
	10	7303.86	338.03	4.42 71.06 66.64	7394.28	343.15	4.44 65.42 60.98
	5	7303.86	338.04	4.42 71.06 66.64	7395.89	342.81	4.43 65.42 60.99
	3	7303.86	338.06	4.42 71.06 66.64	7395.83	342.22	4.42 65.42 61.00
	1	7303.78	338.19	4.43 71.08 66.65	7402.64	342.31	4.42 65.44 61.02
	0.5	7305.36	338.38	4.43 71.12 66.69	7408.75	341.33	4.40 65.52 61.12
	0.2	7304.83	341.73	4.47 71.44 66.97	7404.29	348.79	4.50 65.84 61.34
	0.1	7300.59	351.82	4.60 72.32 67.72	7411.77	367.92	4.73 67.03 62.31
	0.05	7339.24	381.16	4.94 73.63 68.70	7406.36	432.56	5.52 69.56 64.05
	0.03	7341.20	429.50	5.53 74.12 68.60	7376.96	538.35	6.80 71.26 64.45
	0.02	7349.52	485.72	6.20 74.37 68.17	7410.01	642.32	7.98 72.14 64.16
	0.01	7313.96	561.85	7.13 73.98 66.84	7379.45	791.97	9.69 71.92 62.23
	0.001	7239.58	626.28	7.96 73.55 65.59	7322.18	894.65	10.89 71.18 60.30
	0.0001	7383.21	625.69	7.81 73.55 65.73	7395.39	903.41	10.89 71.19 60.30

  is set to 0.05), is reduced by 26.8%, from 521.02 Wh.m -2 to 381.16 Wh.m -2 , while is reduced by 2%, from 7494.29 Wh.m -2 to 7339.24 Wh.m -2 . For houses ( is set to 0.03), is reduced by 31.6%, from 786.73 Wh.m -2 to 538.35 Wh.m -2 , while is reduced by 0.5%, from 7414.05 Wh.m -2 to 7376.96 Wh.m -2 .

Table 3 .

 3 Impact on the evaluation criteria and on energy consumption (offices) of the fuzzification phase and both the number and the design of fuzzy rules (PID-FLC scheme).

	Configuration	Module FLCRE ε Rules ε	Module FLCFE Rules
	PID	-	-	-	-	-	-	-	7494.29 521.02 6.50 72.03 65.53
	A	3	3	3	3	2	3	6	7236.19 473.99 6.15 59.69 53.54
	B	5	5	5	5	2	5	10	7779.79 338.76 4.17 62.07 57.90
	C	5	5	5	5	2	5	10	7504.92 480.95 6.02 72.14 66.12
	D	5	5	5	5	2	3	10	7700.20 619.18 7.44 70.78 63.33
	E	7	5	7	7	2	5	14	7731.35 470.66 5.74 72.38 66.64
	F	7	5	7	7	2	5	14	7470.84 625.22 7.72 71.05 63.32
	G	7	5	7	7	2	5	14	7426.40 654.02 8.09 71.08 62.98
	H	7	5	7	7	2	4	14	7470.84 625.22 7.72 71.05 63.32
	I	7	5	7	7	2	4	14	6709.86 760.13 10.2 69.48 59.31

Table 4 .

 4 Fuzzy rules for module (optimal configuration ).

	Rule	1	2	3	4	5	6	7
	Module FLCRE	NH	NM	NL	AZ	PL	PM	PH
		NH	NH	NL	AZ	PL	PL	PH

Table 5 .

 5 Fuzzy rules for module (optimal configuration ).

		Rule	1	2	3	4	5	6	7
			NH	NM	NL	AZ	PL	PM	PH
	Module		Null	Null	Null	Null	Null	Null	Null
	FLCFE	Rule	8	9	10	11	12	13	14
			NH	NM	NL	AZ	PL	PM	PH
			Null	Null	Null	Weak	Medium	Strong	Full

Table 6 .

 6 Respective performance of the PID, PID-MPC and PID-FLC control schemes (offices).

	Control scheme
	PID	7494.29	521.03	6.50	72.03	65.53
	PID-MPC	7339.24	381.16	4.94	73.63	68.70
	PID-FLC	7731.35	470.66	5.74	72.38	66.64


Paul Avrillier

Laurent Hivert

Francis Kramarz
email: kramarz@ensae.fr

Driven Out of Employment? The Impact of the Abolition of the Draft on Driving Schools and Aspiring Drivers

Before 1997 the compulsory military service was a way for many young French men to obtain their driver's license for free. After the abolition of compulsory conscription in 1997, this sex-based discrimination disappeared. We show that the suppression of such an opportunity was followed by a fall in young men's possession of a car driver's license, particularly for men living in urban areas. Because the causal relation between holding a driver's license and employment is hard to demonstrate, we use this policy change as an instrument for the former. Indeed estimates demonstrate a negative impact of the abolition of the draft on employment for young men and a likely causal impact of holding a driver's license on employment probability. However, we must remain cautious since we cannot fully show that the effect is due to the lack of a driver's license in itself rather than the lack of other abilities the national service could provide (e.g. professional courses). Then, we show that the abolition of the draft also had a more direct and uncontroversial effect on the (heavily regulated) driving schools industry: the shock on driver's license demand that the reform induced resulted in increased rents. These rents translated in an increase in the number of driving schools, a stability in total employment, a decrease in average employment, no increase of total sales or value-added, no obvious decrease in profits per school, but an increase in wages paid to the teachers in those cities that had many young men. Hence, those who benefited from increased demand have been the teachers, in limited supply, not the incumbent schools or the consumers. We believe that such an outcome is potentially inefficient and is due to the heavy entry regulations distorting the economy of this industry.

Introduction

A driver's license is clearly an important liberating asset, more particularly in dispersed countries, such as the United States. It is even more so in France, a country with wide geographical dispersion, because the cost of a driver's license (learning and test costs) is one of the largest in the world. The difficulty of obtaining this license (because of its cost, but not only) has been a matter of much public debate, raising the problem of inequality of access for the poorest and the youngest part of the population. In this paper, we analyze the possible causal effect of possessing a driver's license on employment among different social and geographic groups. These effects should be positive since a driver's license enables the holder to drive to work, particularly in locations where public transportation is scarce, or to use a vehicle for business purposes. If hardly any paper in the existing literature can be found on the subject, it may be because the problem is not seen as essential in many countries but it may also come from data sources that do not have information on possession of a driver's license.

In addition, solving the causality issue is potentially hard because possessing a driver's license is clearly endogenous in relation to employment: one person who has a talent for exams should also easily pass the driver's license tests as another diploma, and has therefore better chances to get a job, not because of the license but because of the talent. Moreover there is a potential reverse causality issue when examining the impact of having a driver's license on employment: having a job makes it easier to pay for your driver's license because of your earnings. As a consequence, it is essential to find good exogenous instruments useable for car driver's license.

Before 1997, young French male adults had to do a ten months military service at the age of 18 or they could defer it if they stayed on in university education. Among them, some had the opportunity to obtain their military driver's license (Brevet de conduite militaire), which could be converted into a usual civilian driver's license. It was a way for a fraction of young French men to pass the driver's license for free, whereas women were not offered such an opportunity. After the abolition of the national service, this sex-based, albeit rarely noted, discrimination disappeared.

The idea here is to use the abolition of compulsory conscription in France as an opportunity to examine several issues, and in particular the impact of having a driver's license on employment. Therefore, the abolition of the draft can be used to derive an instrument for driver's license possession. Indeed the 1997 abolition was followed by a fall in the possession of a driver's license for men relative to women. This fall can then be used to measure the impact of a license on employment.

The first contribution of this paper is to show that the decline in the number of conscripts caused a decrease in the possession of a driver's license for young men after the reform. The fall is identified by comparing the relative driver's license rate between men and women, before and after the reform, within different age groups (difference in difference methodology, in fact a triple difference). In addition, because of the timing, the analysis has a flavor of regression discontinuity. Furthermore it is also possible to identify those who were most affected, as measured by their socio-economic background or their geographic environment (rural or urban). In particular, we show that individuals living in an urban environment were more affected than those living in rural environment.

The second contribution of our paper is to analyze if having a driver's license helps in getting a job. After having shown that the reform provides a valid instrument for holding a driver's license, we use it in our employment IV regressions. Our results indeed show that young men affected by the abolition of the draft found it harder to get a job after this date, 1997. Whether this phenomenon is fully attributable to the lack of a driver's license in itself or to other services the national service could provide (training, behavioral help, etc.), our results cannot tell, as all the maintained assumption in our IV analysis is that the effects of the draft only go through the endogenous variable, i.e. holding a driver's license.

The third and last contribution of our paper is to analyze the impact of the reform on the driving schools industry. As the fraction of women and men differs widely at the local level, in particular in smaller rural towns, the 1997 abolition of conscription can be considered as a natural experiment: specifically as a shock on demand for the driver's license randomly distributed all over the country. All of a sudden, thousands of young men who could acquire the driver's license independently of driving schools now turned to them massively and the precise increase in demand depended on local demography. Because demand for driving schools increased, the supply should have responded. But when an industry experiences such a shock on demand, the effects can be extremely diverse, especially when considering a regulated industry. Indeed, a short-term price increase should be the result but the final outcome depends on various elasticities. One outcome could also be that the "biggest" and most profitable schools attract more customers and offer better conditions; leading to mergers at a local level. In fact, in an industry regulating both schools and diplomas, teachers' supply and firms' entry are going to play a crucial role in the final outcomes, and the precise mechanisms should affect the driver's license price as well as the incentives for driving schools to merge or to be created. In all cases, employment and sales should increase in the cities facing a positive demand shock. However, in contrast with these simple ideas, we observe a dispersion phenomenon, i.e. the creation of new driving schools at a local level with a decrease in the average employment, no increase in total local employment or in total sales and profits. This surprising outcome in this context (where prices remain steadily high) can be understood by the observed increases in wages: teachers capture the rents.

II.

Data and background and the success rate was of around two candidates out of three. In addition to the BEPECASER, a license from the "préfet" (the regional representative of the State) must be obtained: this license is valid 5 years and must then be renewed.

In order to have the right to open a new driving school, one needs to have at least a 3 years experience in teaching driving and to show "an ability to run a firm" since 1999.

The 1997 Reform

The discussions about a potential reform of the military service in France were initiated by Jacques Chirac in 1996. The difficulties encountered by the French army during the Gulf War convinced him of the necessity of a reform. During the Gulf War the French force Daguet was stigmatized for its lack of mobility, its lack of autonomy vis-à-vis the American forces. It became clear that the large number of conscripts made the French army much too static and unable to defend French interests overseas. With French borders no longer under threat, the need for a large army had weakened and the country had thus to replace its conscripts by a smaller, more mobile and better performing professional army.

These concerns were potentially enough to reignite an old debate between a great share of the French left still attached to maintaining a democratic army (perceived as an inheritance of the French Revolution) and a fraction of the right which promoted the idea that defense had to be left in the hands of professionals. In fact this debate never took place. The reform was never discussed as a political choice of decisive and strategic character (Cluzel and Thibault 2005; Paqueteau 1997). Even the parties of the left, a priori the most hostile towards the idea of a professional army (especially the communist party), did not really defend the retention of the national service and they all favoured a deep reform of the compulsory conscription.

Compulsory military service was broadly perceived by the public as a pure waste of time. This year off -away from civilian and social realities -was all the more difficult to accept as it took place at a crucial moment of one's life, between the end of education and the entry into the labor market. At the beginning, the replacement of military service by civil service was considered, but the idea was abandoned. Conscription was finally suspended in 1997 and the military administration stopped using conscripts in August 2001. According to the military administration, the number of conscripts was stable in the early nineties; about 200,000 implying that roughly 40 to 50 percent of each birth cohort was called up by the administration and actually did the national service. After Chirac's initiative in 1996, the number of conscripts declined rapidly from 202,000 in 1996 to almost zero in 2001 (see Figure 1).

III.

Impact of the reform on holding a driver's license 3.1 The negative impact of the reform on holding a driver's license for young men First it must be proved that indeed some men were getting their driver's license while doing their national service. Since the information could not be obtained from the French Ministry of Defense, we had to convince ourselves differently before looking at the data. To do so, it is useful to look at the number of B driver's licenses (French car driver's license)

delivered by driving schools between 1990 and 2006 (data from the French Securité Routière:

see Figure 2). It shows that before 1997 more women than men got their driver's license from a driving school but starting in 1997 the opposite occurred: there are now more men then women who get their driver's license form a driving school. This corroborates the assumption that a new portion of men had to enter a driving school after the reform because they could not expect to obtain their driver's license for free in the army anymore. This will be confirmed with the difference in difference analysis based on our first two data sets. in 2002 (i.e. after the reform) have a lower driver's license possession rate than men of the same age in 1995 (i.e. before the reform), both relative to the evolution of women's rate considered as a trend. Note that the method accounts for a possible general change in demand for driver's license, due for example to a price raise, but that it does not account for a possible trend towards women' emancipation (that point will be examined below with the second source of data). Such a decrease would be a clear signal that our assumption about the impact of the reform on driver's license is true. Tables 1 and2 show regressions of a driver's license indicator (for people aged 18 to 23) on a complete set of indicators for sex, post or pre-reform, socio-economic background (in Table 1) and density of population (in Table 2, more exactly urban area versus rural area).

When considering the whole population, any significant fall can hardly be detected for post-reform young men (Table not shown). However, when interacted with other variables such as living in an urban area, having access to a public transport near home, being from a low socio-economic background (Table 3), the fall turns out to be important and significant.

One interpretation goes as follows: young men living in rural areas have strong incentives to get their driver's license, since no other means of transportation is available, for instance to reach shops, services or working place, whereas young men living in urban areas can often do without a car.

Furthermore, coming from a low socio-economic background makes it more difficult to afford a driver's license (learning and tests fees). As a consequence, one can assume that those young men could not fully compensate the abolition of compulsory conscription, .

3.1.2 Holding a driver's license: the global transport survey data (Ile de France only)

The previous difficulties with our first source force us to study the second piece of data, for Paris and its surrounding region (Paris region called Ile-de-France). Paris region is a compact urban area but with a large diversity of towns and social arrangements. At least, roughly speaking, we can separate Paris city on the one side and the rest of the region on the other side (Paris suburbs and more remote places). A main difference with the previous nationwide data is that we now have only three years of survey: 1983, 1991 and 2001. It is thus not possible to make an annual analysis as before. A way to evidence the expected decrease in post-reform men's driver's license rate is thus to regress a driver's license indicator on a complete set of year-age-gender interactions.

Table 3 shows the impact of age, sex, and generation on driver's license possession.

We analyze the results for different age groups: 23 to 30, 23-24 and 27 to 30, 23-24 and 29-30. We also analyze outcomes for different income categories and places of residence. We only report a fraction of these results. Because we use contrasts with women and previous generations of the same age, the analysis is a triple difference. All regressors are shown in the Table . In particular, indicators are included for men aged 23-24 in 1983, 1991, 2001 as well as for men aged 25 and above. Similarly, indicators are included for women. The F-test in the last line tests for the equality of two differences: the coefficients on men aged 25 and above (the exact content depends on the column) in 2001 minus that for men aged 23-24 in the same year; the same difference for the year 1991. Essentially, we expect to see a much smaller effect for men aged 23-24 in year 2001 because those men were not at risk of being drafted in contrast with all other categories. Indeed, for all categories of men aged 23 and 24, the probability of having a driver's license is smaller in 2001, after the abolition of the draft than before, in 1991, in comparison with older but still young men.

Interestingly, this result also holds for those coming from families with mid-to-high incomes in general and in particular for those living in the Ile-de-France region but not in Paris. All those regressions that are not reported (i.e. for low income, whatever the place of residence and for mid-to-high incomes living within Paris) display coefficients such that the null is accepted. Hence, for these groups, there is no effect and the triple difference has essentially no meaning.

IV.

Impact of the reform on employment

In the following Tables, we analyze the causal effect of having a driver's license on employment outcomes. First, as clearly appears from the preceding discussion, men from the earliest generation should be the only one affected by the abolition of the draft. Furthermore, given the dates of surveys (1983,1991,2001), men aged 23-24 in 2001 should be affected whereas men aged 25 and above should not. Hence, we focus on this discontinuity and contrast the impact of age, sex, and generation on employment and holding a driver's license.

Table 5 shows the OLS effect of having a driver's license on employment for the whole group. For all non-reported groups, the coefficient on this variable is also strongly positive and significant. Hence, there is no need to report them. Of course, this is not our last word because, as already mentioned several times, holding a driver's license is endogenous in this regression: indeed, employment may well cause license holding because employment may well buy the lessons needed to obtain the license (reverse causality). This explains why we present in the next Table (Table 6) our IV results, using men aged 25 and above in 2001 as our instrument. We also present in Appendix (Table A.2) the reduced form regression of employment on the set of exogenous variables, including the instrument.

Again, as in Table 4, we only present results for which the instrument is valid and, indeed, that display significant causal effects of holding a driver's license on holding a job.

The effect is strong, stronger than in the OLS results, as usual. It is also much more imprecise, again a usual feature of IV estimates. For those results that are not reported (low income, and mid-to-high in Paris), all IV estimates are not significantly different from zero (with small coefficients).

The reduced form results show the effect of the instrument on employment. The F-test statistics that is computed at the bottom of the Table is similar to that computed in Table 4.

The instrument is positively and strongly correlated with the employment outcome. Or, conversely, the probability difference in employment between males aged 23-24 and males aged 25 and above is much larger (and negative) in 2001 than it was in 1991.

Indeed, even though the draft is a very likely explanation for this fact, a good instrument is such that all the effects of the draft should go through the endogenous variable, i.e. holding a driver's license. Unfortunately, some doubts must be raised at this stage. For instance, professional courses were offered for the low-educated during the military service.

In addition, Maurin and Xenogiani have shown that university students were tempted to do additional years of education to postpone, and eventually avoid, the draft. They also showed that this was mostly true for a fraction of the students, those with relatively bad prospects within the university system, who abandoned before completing college.

It is however tempting to understand why we find no effect for those living in lowincome families but a clear and strong effect for relatively wealthier men, in particular those whose place of residence is outside Paris (no effect for those living within Paris). Clearly, Paris versus its outskirts (which can be quite far away from Paris, because Ile-de-France is pretty wide) offers much better public transport. We do not have a this stage a fully spelled out explanation for this result.

V. Impact of the reform on driving schools

Another noteworthy effect of the abolition of the draft in France is the effect on the driving schools industry. As explained earlier, this industry is heavily regulated both in terms of entry of new schools and supply of new instructors. Indeed, because the fraction of women and men among the young is likely to differ widely at a local level we can use the 1997 abolition of conscription as a natural experiment, specifically as a shock on demand for driver's licenses, randomly distributed all over the country; All of a sudden, thousands of young men who could acquire the driver's license independently of driving schools now turned to them and the increase in demand was depending on the local (random) demography.

Our data encompass almost all driving schools in France (see part II above). However, because we have access to data on firms and not to data on establishments, small independent firms and groups with multiple establishments cannot be distinguished in the SUSE file; both are equally considered as firms. This can affect our results. Indeed, multi-establishment firms are considered in our data as "normal" firms but have a very large turnover, employment, wages, etc., and located in the city where their head office stands. Obviously, the evolution of such groups has simply no meaning at a local level, in particular when compared to "small" driving schools located at a unique city. In order to put aside groups from our analysis, we concentrate on firms with a turnover below 500,000 French francs (around 75,000 euros).

Results are robust when considering firms below 1 million French francs (around 150,000 euros). Also in order to remain at a local level, we do not include driving schools located in the three largest French cities, namely Paris, Lyon and Marseille.

The results are presented in Tables 8,9, and 10 (Table 10 being a robustness test with fewer variables). In Table 8 we focus on average outcomes in the city when in Table 9 we focus on total outcomes in the city. They show that in cities with a larger fraction of men among the young in the years following 1997, new driving schools were created in larger numbers during these years and the average wages paid in the driving schools in the city strongly increased. More surprisingly, driving schools did not fare better (no significant increase in operating profits, added value or even sales summed over all driving schools within a given city) and employment did not increase in the cities with more young men.

Because supply of instructors was limited, their total number appears to have stayed fixed in a city but some deciding to quit their former employer to create new driving schools (with no employee besides them and therefore no wage information). As a consequence, employers increased wages in order to retain employees. Hence operating profit did not increase. This phenomenon is obviously a direct consequence of the driving schools industry regulations: as it is long and costly to become a monitor: supply cannot respond to the demand as quickly as it should have, had the industry be more competitive.

However, total sales should have grown in cities where the fraction of young men is large around 1997, which is not what we observe. A first explanation could be that some young people turned to the multi-establishment driving schools (that we did not keep in our analysis file). Another explanation could be that the emergence of new driving schools induced a decrease in prices. What can be assumed is that new driving schools offered lower prices in order to attract customers. Indeed, driving schools are a typical case of occupational licensing where reputation drives the market: old driving schools that could prove the quality of their learning can demand higher prices, whereas new ones must first conquer their reputation and set lower prices. Driving schools prices can span a large scale of prices, usually from 500 to 1,000 euros in urban areas, sometimes more in more rural areas. Unfortunately, we have no hint on the evolution of prices in our data to corroborate such hypotheses.

These results tend to show that the specific regulations that weigh on the driving schools industry in France did not allow them to absorb the flow of new young customers after 1997. One can question the justification of such regulations for a profession that remains basically a tutoring, with no particular technical knowledge or practice that would be long to acquire.

VI. Conclusions

Compulsory conscription in France was abolished in November 1997. Before the reform, the military service was a way for some young French men to obtain a car driver's license for free. After the reform that opportunity disappeared. We find that the reform was followed by a significant decline in the relative proportion of men possessing a driver's license. These findings suggest that a substantial fraction of men would have had a driver's license if they were born some years earlier (about 6 %). Additional investigations show that the reform had an impact mostly on young men coming from urban areas who could not or did not have to make up for the decrease in driver's license obtaining.

This policy change provides an instrument to make an IV analysis of the causal effect of having a driver's license on employment. We end up with estimates suggesting that possessing a car driver's license is significantly important to employment for people living in urban areas. One reason for this can be that they often live in places where other relatively cheap means of transportation, like public transports, are available. Yet, our results cannot settle whether the specific difficulties encountered by young men on the job market after 1997 are attributable to the lack of a driver's license in itself or to the lack of other abilities the national service could provide, such as professional courses.

This policy change also represents a natural experiment allowing the analyst to examine the driving schools industry, as thousands of young men who used to pass the driver's license in the army turned to driving schools after 1997. Matching administrative data on firms with the population Census at different dates, we show that the number of driving schools increased in those cities with lots of young men. Wages also increased for those who did not (or could not) create their own school. This was a way used by incumbents to prevent establishment of competing driving schools. In many respects, the abolition of the draft was a real "manna" for driving schools and driving instructors. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Hommes

Femmes

Table 1 The effect of the reform on driver's licenses for men from low socio-economic background The low incomes dummy is a household level information; the self supporting information is inferred from the age of the other people living in the same household.

Table 2

The effect of the reform on driver's licenses for men living in urban areas The "low incomes" dummy is household level information and is interacted with the "non self supporting" dummy in the second column.

Table 4

The effect of the reform on driver's licenses for people living with public transport close by (1983,1991,2001). The F-test is for the equality of (Men in 2001 with age above or equal to 25 minus Men in 2001 with age 23 and 24) with the same difference in 1991. (1983,1991,2001). The F-test is for the equality of (Men in 2001 with age above or equal to 25 minus Men in 2001 with age 23 and 24) with the same difference in 1991.

  3.1.1 Holding a driver's license: the car park data (SOFRES survey)The first data source, the car park data conducted annually between 1995 and 2002, makes it possible to construct annual representative samples of the French population aged 18 to 23, and born either before or after the 1997 reform. Precisely we consider post-reform men as the men born up from 1978: indeed, figures show that conscription started to fall for the 1978 generation, even the 1977 generation. Thus we can examine whether men aged 18 to 23

Figure 1 Figure 2 Source

 12 Figure 1

  2.1 The dataThree sets of data are used in our analysis. The primary source of data comes from a nationwide car park survey conducted on a yearly basis by a French private institute, the

	The third and last source of data is the French national file on firms, called SUSE (for SUSE data, we use the Censuses conducted in France in 1990 and 1999. As we match the
	Système Unifié de Statistiques d'Entreprises, i.e. Unified system on firms statistics) built by SUSE data with the census data, we can design a detailed nationwide map of what happened
	INSEE on an annual basis. The specificity of the SUSE file is to "unify" two sources of data: to driving schools before and after the 1997 military reform.
	fiscal data coming from the French fiscal administration (Direction générale des impôts) on
	one hand, and survey data conducted by the INSEE through the EAE survey (for Enquête 2.2 The driving schools industry
	annuelle des entreprises) on the other hand. It yields precise information on firms' activity
	and firms' structure over time, such as employment structure, accounting data like operating The driving schools industry is heavily regulated, as it proves long and costly to
	profit, value-added, wages paid, sales, etc. known from P&Ls (profit and loss accounts) and become a driving school instructor or to be able to run a driving school. Indeed, to become an
	balance sheets, but also different interesting indicators like industry concentration indices. instructor, one needs to get a State-defined diploma called BEPECASER (Brevet pour
	Finally, the SUSE file includes information on location down to the "city" level (French l'exercice de la profession d'enseignant de la conduite automobile et de la sécurité routière).
	commune), which will enable us to match firms data with population data. The SUSE file At least 600 hours of training are explicitly needed (and more if motorcycle and truck is
	gathers all three firms fiscal regimes information: the BIC regime (for Bénéfices Industriels et added). But only 60 hours out of 600 take place in a driving school to learn the practical
	Commercieux, i.e. Industrial and commercial incomes) for manufacturing, trade or craft
	activities that aim at making profits; the BNC regime (for Bénéfices non commerciaux, i.e.
	Non commercial incomes) for individual professionals like physicians, lawyers etc. and other
	regulated activities such as clerks or auctioneers; finally, the BA regime (for Bénéfices
	agricoles, i.e. Farming incomes) for farming activities. The BNC regime comprises three
	categories depending on turnover: BRN (for Bénéfice réel normal, i.e. Standard real incomes)
	Therefore, we use a second source, the global transport survey for the Paris and Ile de gathers some 800 different variables on firms and is compulsory for firms with turnover
	France region (called EGT for Enquête Globale Transport), conducted in 1983, 1991 and exceeding 763 k! (230 k! for services); RSI (for Régime simplifié d'imposition, i.e.
	2001 by the Paris region transport authority (Direction régionale de l'équipement d'Ile de Simplified tax regime) gathers only some 200 different variables and is destined to firms with
	France). Each year includes about 23,000 individuals and 10,000 households. It provides a turnover ranging between 76,3 k! and 763 k! (27 k! and 230 k! for services); finally, the
	information on household incomes and the date of birth, sex, education level, activity status micro-BIC regime is for firms with a turnover below 76,3 k! (27 k! for services). Our study
	(student, employed, unemployed, retired …), possession of the driver's license and the uses both BRN and RSI data: in so doing, we encompass almost all French driving schools in
	department of residence of each individual (see Table A.1). our study. For the SUSE data, we consider all years between 1992 and 2002. Along with the

SOFRES (for Société Française d'Etudes par Sondages).

The annual sample includes about 7,000 households (10,000 questionnaires sent and a return rate of around 70 percent from every part of France). The sampling plan comes from a collaboration with the French national statistics office, INSEE (Institut de la Statistique et des Etudes Economiques) to be representative of the French population. We use this source from 1995 to 2002, providing information on the date of birth, sex, possession of a driver's license for each individual. It also provides information on households: incomes and details on the level of urbanization around the place of residence. Such data are useful in order to study the impact of the reform of the draft on a national scale. Unfortunately there is no information about individual employment, so that it will be of no help for the second part of our paper. aspects, as the main part of lessons includes very formal issues such as reports, discussions, etc. The training costs amount to 5,000 euros and training lasts 9 months. The exam counts at least 3 different steps over a 6 months period! In 2006, 1,545 BEPECASER were delivered

Table 3

 3 The effect of the reform on driver's licenses for men from low socio-economic background in

	urban areas
				All (low incomes
				considered only for non
		All		self supporting people) Non self supporting only
	Constant	0.685 **	(0.015)	0.698 **	(0.013)	00.679 **	(0.015)
	Born before 1977	0.079 **	(0.021)	0.062 **	(0.018)	00.078 **	(0.022)
	Men born before 1977	0.014**	(0.022)	0.027**	(0.018)	00.012**	(0.023)
	Men born after 1978	0.026**	(0.021)	0.030**	(0.019)	00.028**	(0.021)
	Low incomes born before 1977	-0.083 **	(0.022)	-0.182 **	(0.027)	-0.179 **	(0.029)
	Low incomes born before 1977	-0.056 **	(0.023)	-0.164 **	(0.027)	-0.145 **	(0.028)
	Men * Low incomes born before 1977	-0.007**	(0.032)	0.000**	(0.038)	00.015**	(0.040)
	Men * Low incomes born after 1978	-0.070 **	(0.036)	-0.081 **	(0.040)	-0.079 *	(0.041)
	R squared	0.01		0.03		0.04
	Number of observations	6080		6080		5055
	Source: Car Park Surveys (1995-2002) from the SOFRES
	Sample: people aged 18-23 living in urban areas
	Reading:
	The dependant variable is the driver's license
	* Statistically significant at the .10 level, ** Statistically significant at the .05 level
	Standard errors in parentheses

Table 5

 5

				Driving Licenses and the Draft
		Age 23 -30	Age 23-24 and 27-30	Age 23-24 and 29-30	Age 23 to 30 for Middle-	Age 23 to 30 for Middle-
								High Income Individuals	High Income, Out of Paris
		Estimate	Std Err	Estimate	Std Err	Estimate	Std Err	Estimate	Std Err	Estimate	Std Err
	Intercept	0.7181	0.0214	0.7181	0.0213	0.7181	0.0214	0.7433	0.0240	0.7453	0.0261
	Year 1983	-0.0975	0.0154	-0.0893	0.0191	-0.1043	0.0277	-0.1477	0.0165	-0.1440	0.0178
	Age<25 in 1991	-0.0666	0.0314	-0.0584	0.0333	-0.0733	0.0390	-0.1595	0.0345	-0.1910	0.0369
	Women, age<25 in 1983-1991	0.0150	0.0328	0.0068	0.0346	0.0217	0.0402	0.1329	0.0354	0.1569	0.0381
	Women, age>=25 in 2001	-0.0019	0.0242	0.0006	0.0251	0.0219	0.0287	0.0581	0.0268	0.0657	0.0293
	Women, age>=25 in 1983-1991	0.0211	0.0238	0.0328	0.0250	0.0359	0.0285	0.1024	0.0269	0.1120	0.0292
	Men*(Year 1983)*(age <25)	0.1977	0.0339	0.1895	0.0356	0.2045	0.0410	0.2448	0.0356	0.2590	0.0385
	Men*(Year 1991)*(age <25)	0.2001	0.0434	0.1919	0.0446	0.2068	0.0492	0.3328	0.0487	0.3626	0.0520
	Men*(Year 1983)*(age >=25)	0.2679	0.0288	0.2575	0.0320	0.2641	0.0407	0.3220	0.0312	0.3266	0.0339
	Men*(Year 1991)*(age >=25)	0.1756	0.0240	0.1814	0.0250	0.2052	0.0290	0.1874	0.0269	0.1903	0.0291
	Men*(Year 2001)*(age >=25)	0.1359	0.0246	0.1530	0.0255	0.1668	0.0294	0.1452	0.0270	0.1603	0.0294
	Men*(Year 2001)* (age < 25)	0.0354	0.0303	0.0354	0.0301	0.0354	0.0303	0.0618	0.0337	0.0783	0.0358
	R square	0.05		0.05		0.05		0.05		0.05
	Number of observations	10,240		7,679		4,973		7,301		5,896
	F test, p-value	7.29	0.007	6.94	0.0084	5.54	0.0187	19.73	0.0001	21.73	0.0001
	Notes: Source Enquête Transport Ile de France

Table 6

 6

	Employment and Driving Licenses (OLS)
		Age 23 -30
		Estimate	Std Err
	Intercept	0.5764	0.0131
	Driving license	0.2441	0.0098
	Year 1983	0.0177	0.0153
	Age<25 in 1991	-0.0520	0.0311
	Women, age<25 in 1983-1991	-0.0443	0.0268
	Women, age>=25 in 2001	-0.0189	0.0153
	Women, age>=25 in 1983-1991	-0.0380	0.0147
	Men*(Year 1983)*(age <25)	0.0270	0.0280
	Men*(Year 1991)*(age <25)	-0.0656	0.0387
	Men*(Year 1983)*(age >=25)	0.1007	0.0218
	Men*(Year 1991)*(age >=25)	0.0791	0.0150
	Men*(Year 2001)*(age <25)	-0.1850	0.0236
	R square	0.1
	Number of observations	10,240
	Notes: Source Enquête Transport Ile de France (1983, 1991,
	2001).

Table 7

 7

				Employment and Driving Licenses (IV)
		Age 23 -30	Age 23-24 and 27-30	Age 23-24 and 29-30	Age 23 to 30 for Middle-	Age 23 to 30 for Middle-
								High Income Individuals	High Income, Out of Paris
		Estimate	Std Err	Estimate	Std Err	Estimate	Std Err	Estimate	Std Err	Estimate	Std Err
	Intercept	-1.0340	0.3271	-1.0346	0.3029	-1.0534	0.3192	-0.6793	0.3009	-0.6174	0.2888
	Driving license	2.2049	0.3973	2.2057	0.3662	2.2319	0.3935	1.7722	0.3499	1.6831	0.3305
	Year 1983	0.2088	0.0514	0.1925	0.0532	0.2042	0.0743	0.2175	0.0603	0.1852	0.0574
	Age<25 in 1991	0.0786	0.0740	0.0622	0.0761	0.0731	0.0919	0.1743	0.0857	0.1866	0.0917
	Women, age<25 in 1983-1991	0.1287	0.0690	0.1451	0.0747	0.1356	0.0868	-0.0398	0.0536	-0.0587	0.0555
	Women, age>=25 in 2001	0.1871	0.0538	0.2136	0.0549	0.1769	0.0599	0.0839	0.0365	0.0681	0.0380
	Women, age>=25 in 1983-1991	0.1229	0.0461	0.0964	0.0470	0.0963	0.0572	0.0122	0.0312	0.0124	0.0326
	Men*(Year 1983)*(age <25)	-0.1583	0.0726	-0.1421	0.0742	-0.1563	0.0961	-0.1873	0.0704	-0.1862	0.0707
	Men*(Year 1991)*(age <25)	-0.2555	0.0943	-0.2393	0.0951	-0.2536	0.1141	-0.3728	0.1123	-0.3642	0.1146
	Men*(Year 1983)*(age >=25)	-0.2223	0.0814	-0.1839	0.0805	-0.1782	0.1086	-0.2301	0.0844	-0.1729	0.0800
	Men*(Year 1991)*(age >=25)	-0.0628	0.0440	-0.0493	0.0475	-0.0902	0.0711	-0.0550	0.0401	-0.0484	0.0383
	Men*(Year 2001)*(age <25)	-0.0521	0.0589	-0.0522	0.0593	-0.0531	0.0617	-0.0996	0.0524	-0.1218	0.0518
	R square	0.01		0.02		0.02		0.02		0.02
	Number of observations	10,240		7,679		4,973		7,301		5,896
	Notes: Source Enquête Transport Ile de France (1983, 1991, 2001).

Table 8

 8

	Variable

Table 9

 9 Suse, 1992Suse, to 2002Suse, . C ensus 1990Suse, and 1999. The first 5 regressions use 10,505 observations; the last has 9,056.

	Variable

Table 10

 10 Suse, 1992Suse, to 2002Suse, . C ensus 1990Suse, and 1999. The first 5 regressions use 10,505 observations; the last has 9,056.

	Variable

Notes: Source Enquête Transport Ile de France

Source: Global Transport surveys (1983, 1991 and 2001) from the "Direction Regionale de l'Equipement d'Ile de France"