Academic literature on the topic 'Intelligence artificielle – Consommation d'énergie'
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Journal articles on the topic "Intelligence artificielle – Consommation d'énergie":
Admin - JAIM. "Résumés des conférences JRANF 2021." Journal Africain d'Imagerie Médicale (J Afr Imag Méd). Journal Officiel de la Société de Radiologie d’Afrique Noire Francophone (SRANF). 13, no. 3 (November 17, 2021). http://dx.doi.org/10.55715/jaim.v13i3.240.
Dissertations / Theses on the topic "Intelligence artificielle – Consommation d'énergie":
Ihsane, Imane. "Prévision à court terme et gestion des consommations d'énergie électrique dans l'habitat." Thesis, Nantes, 2020. http://www.theses.fr/2020NANT4019.
In this thesis, a short-term forecasting model of electricity demand based on artificial neural networks was developed. First of all, particular care was taken in the selection of the relevant input variables of this model. Then it was used to reproduce the load curves at the scale of an individual housing and at scale of a region. The comparaison between these two levels of aggregation highlighted the limitations of the indicators for assessing the quality of the forecast. As a result, new indicators adapted to residential load profiles were created. In particular for error detection during peak periods. The thesis work also presents an approach to managing the demand for electrical loads. The particularity of this strategy lies in the consumer’s participation in reducing peak electricity consumption and in benefiting from advantageous off-peak electricity rates, which makes him an active consumer. Emphasis will also be placed on consumer comfort. The principle consists in establishing an allocation of priorities to electrical loads. Depending on these and on a setpoint power to be respected, the algorithm grants a satisfaction (or not) to the requests for activation of the loads. In the absence of sufficient usable power, unmet demand, in particular from heating systems, can impact the thermal comfort of the user. In order to remedy this problem, the management methodology has been improved by combining it with the short-term forecasting of load consumption. This forecast identifies the heaters whose activation will be interrupted in the future and anticipates their activation under power and priority constraints. The results obtained are promising and validate the effectiveness of the proposed methodologies
Chaib, Draa Ismat Yahia. "Optimisation de la consommation d'énergie des systèmes mobiles par l'analyse des besoins de l'utilisateur." Thesis, Valenciennes, 2018. http://www.theses.fr/2018VALE0030/document.
Optimizing energy consumption in modern mobile handled devices plays a crucial role as lowering the power consumption impacts battery life and systemreliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the popularity of power-hungry applications, battery life in mobile devices is an important issue. However, the utilization pattern of large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs and the running application requirements in terms of hardware resources. When these information are used properly, an efficient control of power knobs can be implemented to reduce the energy consumption. This thesis has been achieved in collaboration with Intel Portland
Paudel, Subodh. "Methodology to estimate building energy consumption using artificial intelligence." Thesis, Nantes, Ecole des Mines, 2016. http://www.theses.fr/2016EMNA0237/document.
High-energy efficiency building standards (as Low energy building LEB) to improve building consumption have drawn significant attention. Building standards is basically focused on improving thermal performance of envelope and high heat capacity thus creating a higher thermal inertia. However, LEB concept introduces alarge time constant as well as large heat capacity resulting in a slower rate of heat transfer between interior of building and outdoor environment. Therefore, it is challenging to estimate and predict thermal energy demand for such LEBs. This work focuses on artificial intelligence (AI) models to predict energy consumptionof LEBs. We consider two kinds of AI modeling approaches: “all data” and “relevant data”. The “all data” uses all available data and “relevant data” uses a small representative day dataset and addresses the complexity of building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on either simple physical understanding: Heating Degree Day (HDD), modified HDD or pattern recognition methods: Frechet Distance and Dynamic Time Warping (DTW). Four AI techniques have been considered: Artificial Neural Network (ANN), Support Vector Machine (SVM), Boosted Ensemble Decision Tree (BEDT) and Random forest (RF). In a first part, numerical simulations for six buildings (heat demand in the range [25 – 85 kWh/m².yr]) have been performed. The approach “relevant data” with (DTW, SVM) shows the best results. Real data of the building “Ecole des Mines de Nantes” proves the approach is still relevant
Ouni, Bassem. "Caractérisation, modélisation et estimation de la consommation d'énergie à haut-niveau des OS embarqués." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-01059814.
Rioual, Yohann. "RL-based Energy Management for Autonomous Cyber Physical Systems." Thesis, Lorient, 2019. http://www.theses.fr/2019LORIS533.
The energy management of a cyber physical system is a difficult task because of the complexity of hardware architectures and the use of OS. In addition, these systems are deployed in changing environments where their energy harvesting capacity varies. Over time, their energy consumption is modified due to the ageing of the components. Consumption models designed in the laboratory cannot take into account all possible deployment scenarios and system aging. One approach that is developing is the use of reinforcement learning in which we no longer know the system's consumption model; but thanks to this approach, the system is still able to adapt its operation during its deployment according to the evolution of its environment. Several approaches exist in reinforcement learning. The first part of this thesis is devoted to proposing guidelines to help for selecting the most appropriate approach for a given application and target. The second part of this thesis focuses on the design of the reward we give to our system that will influence its behaviour in its environment. Two reward functions able to adjust the system’s performance according to the energy available are presented. The third and last part of this thesis explores the use of several agents to independently control the different modules of our system. This approach allows a more precise control of energy consumption, reducing memory usage compared to a single agent approach
Cherdo, Yann. "Détection d'anomalie non supervisée sur les séries temporelle à faible coût énergétique utilisant les SNNs." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4018.
In the context of the predictive maintenance of the car manufacturer Renault, this thesis aims at providing low-power solutions for unsupervised anomaly detection on time-series. With the recent evolution of cars, more and more data are produced and need to be processed by machine learning algorithms. This processing can be performed in the cloud or directly at the edge inside the car. In such a case, network bandwidth, cloud services costs, data privacy management and data loss can be saved. Embedding a machine learning model inside a car is challenging as it requires frugal models due to memory and processing constraints. To this aim, we study the usage of spiking neural networks (SNNs) for anomaly detection, prediction and classification on time-series. SNNs models' performance and energy costs are evaluated in an edge scenario using generic hardware models that consider all calculation and memory costs. To leverage as much as possible the sparsity of SNNs, we propose a model with trainable sparse connections that consumes half the energy compared to its non-sparse version. This model is evaluated on anomaly detection public benchmarks, a real use-case of anomaly detection from Renault Alpine cars, weather forecasts and the google speech command dataset. We also compare its performance with other existing SNN and non-spiking models. We conclude that, for some use-cases, spiking models can provide state-of-the-art performance while consuming 2 to 8 times less energy. Yet, further studies should be undertaken to evaluate these models once embedded in a car. Inspired by neuroscience, we argue that other bio-inspired properties such as attention, sparsity, hierarchy or neural assemblies dynamics could be exploited to even get better energy efficiency and performance with spiking models. Finally, we end this thesis with an essay dealing with cognitive neuroscience, philosophy and artificial intelligence. Diving into conceptual difficulties linked to consciousness and considering the deterministic mechanisms of memory, we argue that consciousness and the self could be constitutively independent from memory. The aim of this essay is to question the nature of humans by contrast with the ones of machines and AI
Herzog, Christina. "Contributions à la modélisation avec un système multi agent du transfert technologique en Green IT." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30178/document.
Over the past 5 to 10 years, research is numerous on energy reduction in IT (mainly electricity reduction). Several studies indeed alerted the stakeholders and environmental agencies on the urgency of the problem of the energy consumption of large scale infrastructures, like data centres, clouds or simply companies running servers and lots of IT equipment. This awareness moved from a non-so-important issue to major constraints on the operation of these infrastructures. In some cases, the operational costs reach the investment costs, urging new methodologies to appear in order to reduce costs and ecological impact. As of today, new hardware are developed by equipment manufacturers to decrease these costs. Only few and basic techniques are offered at the software and middleware levels out-of-the-box: But in laboratories, some techniques have proven on synthetic data, dedicated workflows or selected applications, to be able to save energy during the lifetime of an infrastructure, in several contexts, from Cloud to HPC in particular. Unfortunately, the transfer (or even the knowledge of the existence) of these techniques to industries is limited to project partners, innovative companies or large private research centres, able to invest time (thus money) on this topic. In my thesis, I investigate the reasons restraining the large adoption of several research results, from the simpler ones to more elaborated ones and I model the ties and interactions between the actors of the technological transfer. The target field has been restricted to Green IT but the methodology and the developed models can be extended to other domains as well. The idea is to identify, on the scale of technical maturity for wider adoption, what is missing and how to increase the speed of the transfer of scientific knowledge. The methodology is based on the following path: First, identifying the actors involved in the process of technology transfer, and understanding their motivations, their means of actions and their limitations. After a study of the state of the art in the domain of innovation diffusion and innovation management, this phase involved the production and the analysis of a dedicated survey targeting researchers and companies, from different size and turnover, restricted to those working in the Green IT field. Identifying each actor is not sufficient since they all interact; therefore their links and the potential of these links for technology transfer have also been studied carefully in a second phase so as to identify the most important ones, with the same methodology with the actors' identification. From these two phases, a multi-agent system (MAS) has been designed
Lesel, Jonathan. "Optimisation de la consommation énergétique d'une ligne de métro automatique prenant en compte les aléas de trafic à l'aide d'outils d'intelligence artificielle." Thesis, Paris, ENSAM, 2016. http://www.theses.fr/2016ENAM0018/document.
In 2014, as part of the Climate Plan, EU member countries have committed to reduce by 27% their energy consumption. One of the main focal areas consists in increasing the energy efficiency of urban transports. This thesis aims to propose a methodology to reduce the energy consumption of automatic metro lines while integrating traffic disruptions that occur under normal operating conditions. The principle adopted in this work is to maximize the reuse of electrical energy generated during braking of the train, by other trains running on the line. First part is dedicated to the electrical modeling of an automatic metro line and development of methods to calculate power flows between trains and power substations. Then, optimization algorithms are introduced to perform optimization of the most influential operating parameters in an ideal configuration ignoring traffic fluctuations. Finally, a methodology based on learning simulation data is developed in order to achieve optimization of energy consumption integrating traffic disruptions in real time. This last part will thus purchase the objective to provide a decision support to determine optimal dwell times to be carried out by trains in each station, so as to maximize braking energy recovery
Ali, Sadaqat. "Energy management of multi-source DC microgrid systems for residential applications." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0159.
Compared to the alternating current (AC) electrical grid, the direct current (DC) electrical grid has demonstrated numerous advantages, such as its natural interface with renewable energy sources (RES), energy storage systems, and DC loads. It offers superior efficiency with fewer conversion steps, simpler control without skin effect or reactive power considerations. DC microgrids remain a relatively new technology, and their network architectures, control strategies, and stabilization techniques require significant research efforts. In this context, this thesis focuses on energy management issues in a multi-source DC electrical grid dedicated to residential applications. The DC electrical grid consists of distributed generators (solar panels), a hybrid energy storage system (HESS) with batteries and a supercapacitor (SC), and DC loads interconnected via DC/DC power converters. The primary objective of this research is to develop an advanced energy management strategy (EMS) to enhance the operational efficiency of the system while improving its reliability and sustainability. A hierarchical simulation platform of the DC electrical grid has been developed using MATLAB/Simulink. It comprises two layers with different time scales: a local control layer (time scale of a few seconds to minutes due to converter switching behavior) for controlling local components, and a system-level control layer (time scale of a few days to months with accelerated testing) for long-term validation and performance evaluation of the EMS. In the local control layer, solar panels, batteries, and the supercapacitor have been modeled and controlled separately. Various control modes, such as current control, voltage control, and maximum power point tracking (MPPT), have been implemented. A low-pass filter (LPF) has been applied to divide the total HESS power into low and high frequencies for the batteries and supercapacitor. Different LPF cutoff frequencies for power sharing have also been studied. A combined hybrid bi-level EMS and automatic sizing have been proposed and validated. It mainly covers five operational scenarios, including solar panel production reduction, load reduction, and three scenarios involving HESS control combined with supercapacitor state of charge (SOC) control retention. An objective function that considers both capital expenditure (CAPEX) and operating costs (OPEX) has been designed for EMS performance evaluation. The interaction between the HESS and EMS has been jointly studied based on an open dataset of residential electrical consumption profiles covering both summer and winter seasons. Finally, an experimental platform of a multi-source DC electrical grid has been developed to validate the EMS in real-time. It comprises four lithium-ion batteries, a supercapacitor, a programmable DC power supply, a programmable DC load, corresponding DC/DC converters, and a real-time controller (dSPACE/Microlabbox). Accelerated tests have been conducted to verify the proposed EMS in different operational scenarios by integrating real solar panels and load consumption profiles. The hierarchical simulation and experimental DC electrical grid platforms can be generally used to verify and evaluate various EMS
Bouabdallaoui, Yassine. "Introduction de l'intelligence artificielle dans le secteur de la construction : études de cas du Facility Management." Electronic Thesis or Diss., Centrale Lille Institut, 2021. http://www.theses.fr/2021CLIL0022.
The industry of Facility Management (FM) has known a rapid advancement through the last decades which leads to a largeexpansion of FM activities. The FM organisations have evolved from the traditional role of providing maintenance services toinclude complex and interconnected activities involving people, processes and technologies. As a consequence of thisexponential growth, facility managers are dealing with growing and varied challenges ranging from energy efficiency andenvironmental challenges to service customisation and customer satisfaction. The development of Artificial Intelligence (AI)is offering academics and practitioners a new set of tools to address these challenges. AI is enabling multiple solutions suchas automation, improving predictability and forecasting and offering services customisation. The Facility Managementindustry can benefit from these new techniques to better manage their assets and improve their processes. However, theintegration of AI into the FM ecosystem is a challenging task that needs to overcome the gap between the business driversand the AI. To unlock the full potential of data analytics and AI in the FM industry, significant work is needed to overcomethe issues of data quality and data management in the FM sector. The overall aim of this thesis is to conceptualise thetheoretical and practical understanding and implementation of artificial intelligence and data-driven technologies into FacilityManagement activities to leverage data and optimise facilities usage. Promises of AI implementations were presented alongwith the challenges and the barriers limiting the development of AI in the FM sector. To resolve these issues, a frameworkwas proposed to improve data management and leverage AI in FM. Multiple case studies were selected to address thisframework. The selected case studies covered predictive maintenance, virtual assistant and natural language processingapplications. The results of this work demonstrated the potential of AI to address FM challenges such in maintenancemanagement and waste management. However, multiple barriers limiting the development of AI in the FM sector wereidentified including data availability issues
Books on the topic "Intelligence artificielle – Consommation d'énergie":
Tripathi, Suman Lata, Sanjeevikumar Padmanaban, Dushyant Kumar Singh, and P. Raja. Design and Development of Efficient Energy Systems. Wiley & Sons, Incorporated, John, 2021.
Tripathi, Suman Lata, Sanjeevikumar Padmanaban, Dushyant Kumar Singh, and P. Raja. Design and Development of Efficient Energy Systems. Wiley & Sons, Limited, John, 2021.
Tripathi, Suman Lata, Sanjeevikumar Padmanaban, Dushyant Kumar Singh, and P. Raja. Design and Development of Efficient Energy Systems. Wiley & Sons, Incorporated, John, 2021.
Book chapters on the topic "Intelligence artificielle – Consommation d'énergie":
ERTZ, Myriam, Shouheng SUN, Émilie BOILY, Gautier Georges Yao QUENUM, Kubiat PATRICK, Yassine LAGHRIB, Damien HALLEGATTE, Julien BOUSQUET, and Imen LATROUS. "Les produits augmentés : la contribution de l’industrie 4.0 à la consommation durable." In Le marketing au service du développement durable, 277–300. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9036.ch14.