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Real-time estimates of Swiss electricity savings using streamed smart meter data

Author

Listed:
  • Mari, Alessandro
  • Remlinger, Carl
  • Castello, Roberto
  • Obozinski, Guillaume
  • Quarteroni, Silvia
  • Heymann, Fabian
  • Galus, Matthias

Abstract

The gas crisis of 2022 put pressure on electricity prices in Europe, prompting the Swiss government to launch a national energy-saving campaign. To effectively quantify potential savings and guide timely decision-making, this campaign called for rigorous near-real-time modeling of changes in electricity consumption habits. The proposed approach estimates national electricity consumption at an hourly resolution across three consumer categories using thousands of streamed smart-meter load curves. These curves are aggregated to produce a national consumption estimate using scaling factors that account for differences among Swiss distributors. These factors are derived by regressing historical annual consumption against public socio-economic variables. The obtained national load curve is adjusted for the influence of weather conditions, the calendar and global trends, in order to compare different periods with a reference scenario. Such external effects are modeled with splines using Generalized Additive Models, trained on a 5-year dataset, to precisely measure each contribution on the national consumption and evaluate the consumers’ response to the saving plan. The results indicate a reduction of approximately 4.8% of the adjusted electricity consumption during winter 2022–2023, equivalent to an average monthly savings of 246 GWh, distributed across residential, service, and industrial sectors.

Suggested Citation

  • Mari, Alessandro & Remlinger, Carl & Castello, Roberto & Obozinski, Guillaume & Quarteroni, Silvia & Heymann, Fabian & Galus, Matthias, 2025. "Real-time estimates of Swiss electricity savings using streamed smart meter data," Applied Energy, Elsevier, vol. 377(PC).
  • Handle: RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924019202
    DOI: 10.1016/j.apenergy.2024.124537
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    References listed on IDEAS

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    1. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Krarti, Moncef & Aldubyan, Mohammad, 2021. "Review analysis of COVID-19 impact on electricity demand for residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Guo, Zhifeng & O'Hanley, Jesse R. & Gibson, Stuart, 2022. "Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland," Utilities Policy, Elsevier, vol. 79(C).
    5. Al-Mansour, Fouad, 2011. "Energy efficiency trends and policy in Slovenia," Energy, Elsevier, vol. 36(4), pages 1868-1877.
    6. Nedellec, Raphael & Cugliari, Jairo & Goude, Yannig, 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models," International Journal of Forecasting, Elsevier, vol. 30(2), pages 375-381.
    7. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
    8. Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
    9. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
    10. Oliver Ruhnau & Clemens Stiewe & Jarusch Muessel & Lion Hirth, 2023. "Natural gas savings in Germany during the 2022 energy crisis," Nature Energy, Nature, vol. 8(6), pages 621-628, June.
    11. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    12. Andersen, F.M. & Gunkel, P.A. & Jacobsen, H.K. & Kitzing, L., 2021. "Residential electricity consumption and household characteristics: An econometric analysis of Danish smart-meter data," Energy Economics, Elsevier, vol. 100(C).
    13. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    14. Gallo Cassarino, Tiziano & Sharp, Ed & Barrett, Mark, 2018. "The impact of social and weather drivers on the historical electricity demand in Europe," Applied Energy, Elsevier, vol. 229(C), pages 176-185.
    15. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    Full references (including those not matched with items on IDEAS)

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