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

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  • Dampeyrou, Charles
  • Goichon, Antoine
  • Ghienne, Martin
  • Tschannen, Valentin
  • Schaack, Sofiane

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.

Suggested Citation

  • Dampeyrou, Charles & Goichon, Antoine & Ghienne, Martin & Tschannen, Valentin & Schaack, Sofiane, 2024. "Unsupervised separation of the thermosensitive contribution in the power consumption at a country scale," Applied Energy, Elsevier, vol. 363(C).
    • Handle: RePEc:eee:appene:v:363:y:2024:i:c:s030626192400480x
    DOI: 10.1016/j.apenergy.2024.123097
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    References listed on IDEAS

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    1. Zhao, Bochao & Ye, Minxiang & Stankovic, Lina & Stankovic, Vladimir, 2020. "Non-intrusive load disaggregation solutions for very low-rate smart meter data," Applied Energy, Elsevier, vol. 268(C).
    2. Burillo, Daniel & Chester, Mikhail V. & Ruddell, Benjamin & Johnson, Nathan, 2017. "Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves," Applied Energy, Elsevier, vol. 206(C), pages 267-277.
    3. Harish, Santosh & Singh, Nishmeet & Tongia, Rahul, 2020. "Impact of temperature on electricity demand: Evidence from Delhi and Indian states," Energy Policy, Elsevier, vol. 140(C).
    4. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    5. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
    6. Jovanović, Saša & Savić, Slobodan & Bojić, Milorad & Djordjević, Zorica & Nikolić, Danijela, 2015. "The impact of the mean daily air temperature change on electricity consumption," Energy, Elsevier, vol. 88(C), pages 604-609.
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