De l'instrumentation au contrôle optimal prédictif pour la performance énergétique du bâtiment

More efficient energy management of buildings through the use of Model Predictive Control(MPC) techniques is a key issue to reduce the environmental impact of buildings. Buildingenergy performance is currently improved by using renewable energy sources, a betterdesign of the building envelope (insulation) and the use of advanced management systems.The more the design aims for high performance, the more interactions and coupling effectsbetween the building, its environment and the conditions of use are important and unintuitive.Only a more integrated regulation would take in account this complexity, and couldhelp to optimize the consumption without compromising the comfort.Model Predictive Control techniques, based on the use of dynamic models and optimizationmethods, promise a reduction of consumption and discomfort. They can generate energysavings by anticipating the evolution of renewable sources and intermittent needs, while takingadvantage of the building thermal inertia and other storage items. However, in the caseof buildings, obtaining a good dynamic model is tough, due to important uncertainties onmodel parameters and system solicitations.Recent advances in the field of wireless sensor networks are fostering the deployment ofsensors in buildings, and offer a promising opportunity to reduce these errors. Nevertheless,designing a sensor network dedicated to MPC is not obvious, and energy monitoring,instrumentation, modeling and predictive control matters must be considered jointly.This thesis aims at establishing the links between MPC and instrumentation needs inbuildings. We propose a generic method for building modeling, thermal simulation andoptimization. This methodology involves a multi-zone thermal model of the building, andefficient optimization algorithms using an adjoint model and tools from the optimal controltheory. It was implemented in a specific toolbox to develop a predictive control strategywith optimal control phases, state estimation phases and model calibration.At first, we study the formulation and resolution of an optimal control problem. We discussthe differences between such a control and a conventional regulation strategy, throughperformance indicators. Then, we present a state estimation method based on the identificationof unknown internal gains. This estimation method is subsequently coupled with theoptimal control method to form a predictive control strategy.As the parameters values of a building model are often very uncertain, parametric modelcalibration is essential to reduce prediction errors and to ensure the MPC performance. Consequently,we apply our methodology to a calibration technique based on in situ temperaturemeasurements. We also discuss how our approach can lead to selection techniques in orderto choose calibrated parameters and sensors for MPC purposes.Eventually, the predictive control strategy was implemented on an experimental building,at CEA INES, near Chambéry. The entire building was modeled, and the different steps ofthe control strategy were applied sequentially through an online supervisor. This experimentgave us a useful feedback on our methodology on a real case.This thesis is the result of a collaboration between CEA Leti, IFSTTAR Nantes andG2ELab, and is part of the ANR PRECCISION project.Face aux forts besoins de réduction de la consommation énergétique et de l’impact environnemental,le bâtiment d’aujourd’hui vise la performance en s’appuyant sur des sourcesd’énergie de plus en plus diversifiées (énergies renouvelables), une enveloppe mieux conçue(isolation) et des systèmes de gestion plus avancés. Plus la conception vise la basse consommation,plus les interactions entre ses composants sont complexes et peu intuitives. Seule unerégulation plus intégrée permettrait de prendre en compte cette complexité et d’optimiser lefonctionnement pour atteindre la basse consommation sans sacrifier le confort.Les techniques de commande prédictive, fondées sur l’utilisation de modèles dynamiqueset de techniques d’optimisation, promettent une réduction des consommations et de l’inconfort.Elles permettent en effet d’anticiper l’évolution des sources et des besoins intermittentstout en tirant parti de l’inertie thermique du bâtiment, de ses systèmes et autres élémentsde stockage. Cependant, dans le cas du bâtiment, l’obtention d’un modèle dynamique suffisammentprécis présente des difficultés du fait d’incertitudes importantes sur les paramètresdu modèle et les sollicitations du système. Les avancées récentes dans le domaine de l’instrumentationdomotique constituent une opportunité prometteuse pour la réduction de cesincertitudes, mais la conception d’un tel système pour une telle application n’est pas triviale.De fait, il devient nécessaire de pouvoir considérer les problématiques de monitoring énergétique,d’instrumentation, de commande prédictive et de modélisation de façon conjointe.Cette thèse vise à identifier les liens entre commande prédictive et instrumentation dansle bâtiment, en proposant puis exploitant une méthode générique de modélisation du bâtiment,de simulation thermique et de résolution de problèmes d’optimisation. Cette méthodologiemet en oeuvre une modélisation thermique multizone du bâtiment, et des algorithmesd’optimisation reposant sur un modèle adjoint et les outils du contrôle optimal. Elle a étéconcrétisée dans un outil de calcul permettant de mettre en place une stratégie de commandeprédictive comportant des phases de commande optimale, d’estimation d’état et decalibration.En premier lieu, nous étudions la formulation et la résolution d’un problème de commandeoptimale. Nous abordons les différences entre un tel contrôle et une stratégie de régulationclassique, entre autres sur la prise en compte d’indices de performance et de contraintes. Nousprésentons ensuite une méthode d’estimation d’état basée sur l’identification de gains thermiquesinternes inconnus. Cette méthode d’estimation est couplée au calcul de commandeoptimale pour former une stratégie de commande prédictive.Les valeurs des paramètres d’un modèle de bâtiment sont souvent très incertaines. Lacalibration paramétrique du modèle est incontournable pour réduire les erreurs de prédictionet garantir la performance d’une commande optimale. Nous appliquons alors notreméthodologie à une technique de calibration basée sur des mesures de températures in situ.Nous ouvrons ensuite sur des méthodes permettant d’orienter le choix des capteurs à utiliser(nombre, positionnement) et des paramètres à calibrer en exploitant les gradients calculéspar la méthode adjointe.La stratégie de commande prédictive a été mise en oeuvre sur un bâtiment expérimentalprès de Chambéry. Dans le cadre de cette étude, l’intégralité du bâtiment a été modélisé,et les différentes étapes de notre commande prédictive ont été ensuite déployées de mainière séquentielle. Cette mise en oeuvre permet d’étudier les enjeux et les difficultés liées àl’implémentation d’une commande prédictive sur un bâtiment réel.Cette thèse est issue d’une collaboration entre le CEA Leti, l’IFSTTAR de Nantes et leG2ELab, et s’inscrit dans le cadre du projet ANR PRECCISION

Replicator population dynamics of group interactions: Broken symmetry, thresholds for metastability, and macroscopic behavior

The effect of group structure on cooperative behavior is not well understood. In this paper, we study the dynamics of a public goods game involving n-agent interactions. In the proposed setup, the population is organized into groups. We associate the individual fitness to group performance, while the evolutionary dynamics takes place globally. We derive analytical expressions and show that the model exhibits several fixed points, including the symmetric homogeneous states of total cooperation and total defection, which are unstable and stable, respectively. Interestingly, even if both individual and group levels are organized as well-mixed populations, the dynamics displays intermediate values of cooperation under the replicator dynamics. Namely, as soon as one of the groups, at least, is fully cooperative, intermediary fixed points appear for the rest of the groups. In addition to the analytical approach, we have performed numerical simulations that reproduce the internal fixed points obtained theoretically, showing coexisting intermediate levels of cooperation. Potential implications of these results in terms of group selection and the role of social norms are also discussed

Irregular sleep habits, regional grey matter volumes, and psychological functioning in adolescents

Changing sleep rhythms in adolescents often lead to sleep deficits and a delay in sleep timing between weekdays and weekends. The adolescent brain, and in particular the rapidly developing structures involved in emotional control, are vulnerable to external and internal factors. In our previous study in adolescents at age 14, we observed a strong relationship between weekend sleep schedules and regional medial prefrontal cortex grey matter volumes. Here, we aimed to assess whether this relationship remained in this group of adolescents of the general population at the age of 16 (n = 101; mean age 16.8 years; 55% girls). We further examined grey matter volumes in the hippocampi and the amygdalae, calculated with voxel-based morphometry. In addition, we investigated the relationships between sleep habits, assessed with self-reports, and regional grey matter volumes, and psychological functioning, assessed with the Strengths and Difficulties Questionnaire and tests on working memory and impulsivity. Later weekend wake-up times were associated with smaller grey matter volumes in the medial prefrontal cortex and the amygdalae, and greater weekend delays in wake-up time were associated with smaller grey matter volumes in the right hippocampus and amygdala. The medial prefrontal cortex region mediated the correlation between weekend wake up time and externalising symptoms. Paying attention to regular sleep habits during adolescence could act as a protective factor against the emergence of psychopathology via enabling favourable brain development.Peer reviewe

Sex effects on structural maturation of the limbic system and outcomes on emotional regulation during adolescence

Though adolescence is a time of emerging sex differences in emotions, sex-related differences in the anatomy of the maturing brain has been under-explored over this period. The aim of this study was to investigate whether puberty and sexual differentiation in brain maturation could explain emotional differences between girls and boys during adolescence. We adapted a dedicated longitudinal pipeline to process structural and diffusion images from 335 typically developing adolescents between 14 and 16 years. We used voxel-based and Regions of Interest approaches to explore sex and puberty effects on brain and behavioral changes during adolescence. Sexual differences in brain maturation were characterized by amygdala and hippocampal volume increase in boys and decrease in girls. These changes were mediating the sexual differences in positive emotional regulation as illustrated by positive attributes increase in boys and decrease in girls. Moreover, the differential maturation rates between the limbic system and the prefrontal cortex highlighted the delayed maturation in boys compared to girls. This is the first study to show the sex effects on the differential cortico/subcortical maturation rates and the interaction between sex and puberty in the limbic system maturation related to positive attributes, reported as being protective from emotional disorders.Peer reviewe

fMRI of reward processing in a community-based longitudinal study

Up to 40% of youth with autism spectrum disorder (ASD) also suffer from anxiety, and this comorbidity is linked with significant functional impairment. However, the mechanisms of this overlap are poorly understood. We investigated the interplay between ASD traits and anxiety during reward processing, known to be affected in ASD, in a community sample of 1472 adolescents (mean age=14.4 years) who performed a modified monetary incentive delay task as part of the Imagen project. Blood-oxygen-level dependent (BOLD) responses to reward anticipation and feedback were compared using a 2x2 analysis of variance test (ASD traits: low/high; anxiety symptoms: low/high), controlling for plausible covariates. In addition, we used a longitudinal design to assess whether neural responses during reward processing predicted anxiety at 2-year follow-up. High ASD traits were associated with reduced BOLD responses in dorsal prefrontal regions during reward anticipation and negative feedback. Participants with high anxiety symptoms showed increased lateral prefrontal responses during anticipation, but decreased responses following feedback. Interaction effects revealed that youth with combined ASD traits and anxiety, relative to other youth, showed high right insula activation when anticipating reward, and low right-sided caudate, putamen, medial and lateral prefrontal activations during negative feedback (all clusters PFWE<0.05). BOLD activation patterns in the right dorsal cingulate and right medial frontal gyrus predicted new-onset anxiety in participants with high but not low ASD traits. Our results reveal both quantitatively enhanced and qualitatively distinct neural correlates underlying the comorbidity between ASD traits and anxiety. Specific neural responses during reward processing may represent a risk factor for developing anxiety in ASD youth

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SEP NESTER S2I translated by A.M. Dujardi

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SEP NESTER S2

ReTrofiT: A Software to Solve Optimization and Identification Problems Applied to Building Energy Management

International audienceEnergy management systems in buildings greatly contribute to the improvement of overall energy efficiency. Monitoring systems can lead to significant reductions of the global energy use by increasing occupants' awareness of the consumptions or by enabling the implementation of more efficient regulation strategies. Model predictive control consists in computing optimal heating or cooling strategies by taking into account the future evolution of the state of the building under forecast weather or use conditions. Demand response strategies in smart grids consist in adjusting energy demand at the end-user level to reduce the overall demand thus resulting in end-user customer bill savings, increase of electricity market stability and of electricity supply reliability. Further, today the building construction practices evolve towards a more performance-based approach in which the concern becomes the performance of the final building rather than the means employed to construct it. All the aforementionned applications rely on the ability to accurately predict a building's behavior using a calibrated model. In building energy applications, uncertainties in input data of modelling tools often lead to important discrepancies between the model predictions and the real performance. The desired model response can be obtained if the internal parameters of the model are calibrated using on-site measurements and model identification methods. The paper presents the software ReTrofiT that was specifically designed to treat this kind of problems. ReTrofiT is first of all a dynamic building simulation code with multizone-type assumptions. It integrates a set of tools and algorithms to set up and solve minimization problems as well as to compute sensitivities. All these operations can be done with a negligible computation by means of the adjoint model that is intrinsically implemented

The empirical replicability of task-based fMRI as a function of sample size

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these