Surveillance et diagnostic de charges électriques résidentielles
Bibliographic record
Abstract
Smart grid is the key enabler of the main conceptual framework for Smart Home (SH) concept.The emergence of SH yields Home Energy Management Systems (HEMS) to enhance residential energy solutions.HEMS are capable of creating an automation network to provide many energy saving applications and customer comfort facilitation through Appliance Load Monitoring and Diagnosis (ALMD) technologies.ALMD enables load decomposition at the appliance level.An appliance-Ievel analysis can bene fit both customers and utilities by improving energy efficiency and subsequently reducing the electricity cost.The ALMD system accounts for two main procedures of load identification and fauIt detection that lie at the root of this study.The former is offered by load monitoring phase.This phase can be executed by use of intrusive and non-intrusive methods.Nevertheless, due to different issues related to the first technique, the non-intrusive mechanism has been promoted.On the other side, the latter is realized through anomal y detection manners.These manners exploit the results of load monitoring step to detect any deviation in appliances' normal behavior.Consequently, the anomalous appliances are analyzed to be diagnosed in terms of either faulty or abnormal.Accordingly, this essay commences with the first phase, in the context of Non-intrusive Load Monitoring (NILM).NILM entails essential prerequisites in order to realize a fruitful structure.These essentials generally vary based on customer's choice of appliances, their electrical characteristics, and environ mental conditions.For example, in Quebec, Canada, where this study is conducted, the Electric Space Heaters (ESH) and Electric Water Heaters 1 would like to express my sincere gratitude to my research supervisor, Professor Kodjo Agbossou, the faculty member of Department of Electrical and Computer Engineering of UQTR for his invaluable research support, outstanding advice, patience, and motivation.He taught me how to conduct scientific research, how to present a scientific study, and how to write a scientific manuscript.His endless guidance and immense knowledge helped me in all the time of my research studies. 1 wou Id also like to extent thanks to my research advisor, Professor Sousso Kelouwani, the faculty member of Department of Mechanical Engineering of UQTR for his continuous help, cooperation, and inspiration.His professional support and precious scientific knowledge have made a deep impression on me.His dedication to providing high-quality research credits him with an excellent scientist ex ample to follow and goal to strive to. 1 am deeply indebted to Professor Alben Cardenas, the faculty member of Department of Electrical and Computer Engineering of
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".