 Unsupervised separation of the thermosensitive contribution in the power consumption at a country scaleCharles Dampeyrou, Antoine Goichon, Martin Ghienne, Valentin Tschannen and Sofiane Schaack Applied Energy, 2024, vol. 363, issue C, No S030626192400480X Abstract: 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. Keywords: Thermosensitivity; Electric energy modeling; Non-Intrusive load disaggregation; Deep-learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:363:y:2024:i:c:s030626192400480x Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2024.123097 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.