Unsupervised separation of the thermosensitive contribution in the power consumption at a country scale

A large part of French electricity consumption variation is due to temperature fluctuations. While HVAC (heating, ventilation and air-conditioning) systems consumption are directly affected by the temperature, other systems (refrigerator, freezer, water heater) can also be driven by weather changes...

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Veröffentlicht in:Applied energy Jg. 363; S. 123097
Hauptverfasser: Dampeyrou, Charles, Goichon, Antoine, Ghienne, Martin, Tschannen, Valentin, Schaack, Sofiane
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2024
Elsevier
Schlagworte:
Computer Science
Deep-learning
Electric energy modeling
Engineering Sciences
Non-Intrusive load disaggregation
Thermosensitivity
Electric energy modeling
Non-Intrusive load disaggregation
Thermosensitivity
Deep-learning
Thermosensitivity Electric energy modeling Non-Intrusive load disaggregation Deep-learning
ISSN:0306-2619, 1872-9118
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Zusammenfassung:A large part of French electricity consumption variation is due to temperature fluctuations. While HVAC (heating, ventilation and air-conditioning) systems consumption are directly affected by the temperature, other systems (refrigerator, freezer, water heater) can also be driven by weather changes making thermal contribution to overall consumption difficult to extract. This paper presents a “by-design” unsupervised data-driven method to separate the consumptions due to the weather in the overall electricity consumption. The proposed deep-learning model is based on the separation of meteorological parameters from calendar ones within the model architecture. The performances of this model, in particular its ability to split consumption mechanisms, is tested on a synthetic dataset and on the french consumption dataset. Being relatively simple and interpretable, this approach can be generalized to other countries whereasenergy sobriety represents an important challenge we are facing. •The hourly power load of a country due to temperature is unsupervisingly identified.•Deep learning model provides Large Scale Non Intrusive Load Separation.•Engineering-driven neural network design separates weather and calendar consumption.•Model's performance is assessed on country's actual electricity consumption.
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2024.123097