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Record W7078616320

Participation des clients à la gestion optimisée des charges électriques : modélisation, simulation et analyse dans un cadre résidentiel

2025· other· fr· W7078616320 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2025
Typeother
Languagefr
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Statistical analysisSocial impact
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Depuis son introduction à grande échelle au XIXe siècle, l’électricité est devenue essentielle pour soutenir le développement technologique et améliorer la qualité de vie des humains. Cependant, son utilisation massive présente une particularité : l’énergie produite doit être consommée immédiatement. Cela entraîne des contraintes physiques ainsi que des problématiques telles que la congestion des lignes de transmission et l’inefficacité des centrales, qui entraînent une hausse des coûts énergétiques lors des périodes de pointe de consommation. Dans ce contexte, plusieurs études se concentrent sur la gestion des pointes de consommation afin d’atténuer ces problèmes. Elles proposent la construction de centrales plus efficaces, le recours de technologies respectueuses de l’environnement, le renforcement des réseaux de transmission et de distribution, le développement de bâtiments intelligents et des initiatives visant à encourager les consommateurs à mieux gérer leur demande énergétique lors des périodes de pointe. Compte tenu de ces éléments, la présente recherche se concentre sur le développement de systèmes de gestion de l’énergie visant à encourager la participation active des clients à la gestion des charges électriques, notamment à travers leur implication sur les marchés énergétiques, grâce à une interface homme-machine logicielle. Une attention particulière est portée au cas du Québec. L’objectif principal est de développer un modèle d’optimisation capable de contrôler les charges dans le contexte d’une maison intelligente équipée de (RED), et d’étendre ce contrôle à l’échelle d’un quartier comportant plusieurs RED. L’étude vise également à analyser en profondeur les leviers de motivation des clients afin qu’ils puissent contribuer à la stabilité du réseau en adaptant leur consommation d’énergie en fonction ressources énergétiques distribuées de la demande. Cette structure de contrôle profite à la fois aux consommateurs, en leur permettant de réaliser des économies, au marché, en améliorant l’efficacité énergétique, à l’environnement, en réduisant les émissions de gaz à effet de serre, et enfin au réseau, en diminuant les pointes de consommation ainsi que la congestion. Une attention particulière est accordée aux mécanismes et programmes existants au Québec qui encouragent cette participation active, tels que les tarifs dynamiques, les programmes incitatifs de réponse à la demande et les technologies de contrôle intelligent. ABSTRACT: Since its large-scale introduction in the 19th century, electricity has become essential for supporting technological development and improving human quality of life. However, its massive use presents a particular challenge: the energy produced must be consumed immediately. This leads to physical constraints as well as issues such as transmission line congestion and inefficient power plants, resulting in increased energy costs during peak consumption periods. In this context, numerous studies focus on managing consumption peaks to alleviate these problems. They propose building more efficient power plants, using environmentally friendly technologies, strengthening transmission and distribution networks, developing smart buildings, and launching initiatives to encourage consumers to better manage their energy demand during peak periods. Based on these considerations, the present research focuses on developing energy management systems that encourage active customer participation in managing electric loads, particularly through involvement in energy markets and via a software-based human-machine interface. Special attention is given to the case of Québec. The main objective is to develop an optimization model capable of controlling loads in a smart home environment equipped with distributed energy resources (DERs), and to extend this control to the neighborhood level with multiple DERs. The study also aims to deeply analyze the motivational levers that can encourage customers to contribute to grid stability by adjusting their energy consumption according to demand. This control framework benefits consumers by enabling cost savings, supports the energy market by improving efficiency, protects the environment by reducing greenhouse gas emissions, and helps the grid by lowering peak loads and network congestion. Special emphasis is placed on existing mechanisms and programs in Québec that promote active participation, such as dynamic pricing, demand response incentive programs, and smart control technologies. A rigorous assessment of economic and environmental benefits shows that load management can reduce costs, mitigate demand, and enhance energy efficiency without compromising customer comfort.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.271
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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