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Statistical Analysis of Baseline Load Models for Residential Buildings in the Context of Winter Demand Response

Author

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  • Alain Poulin

    (Institut de Recherche d’Hydro-Québec (IREQ), 600 av de la Montagne, Shawinigan, QC G9N 7N5, Canada)

  • Marie-Andrée Leduc

    (Institut de Recherche d’Hydro-Québec (IREQ), 600 av de la Montagne, Shawinigan, QC G9N 7N5, Canada)

  • Michaël Fournier

    (Institut de Recherche d’Hydro-Québec (IREQ), 600 av de la Montagne, Shawinigan, QC G9N 7N5, Canada)

Abstract

By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates for related demand response (DR) initiatives. The impact of such initiatives is determined by comparing the actual consumption during a DR event to a baseline, i.e., the estimated consumption that would have occurred in the absence of an event. This paper explores the challenges associated with modeling a baseline in the context of residential winter DR programs with individual performance-based incentives. A sample of more than a thousand residential load profiles was used in this study to provide a statistical comparison of performance metrics for different baseline load models. Arithmetic, regression based, and matching-day models were considered. Results show that adjusted arithmetic models achieve similar performances to the more complex regression model without the need for weather data. These simpler models were also found to be less sensitive to the number of events called during the season. Performing individual adjustments for each of the two daily peak periods also provides better accuracy.

Suggested Citation

  • Alain Poulin & Marie-Andrée Leduc & Michaël Fournier, 2022. "Statistical Analysis of Baseline Load Models for Residential Buildings in the Context of Winter Demand Response," Energies, MDPI, vol. 15(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4441-:d:841961
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    References listed on IDEAS

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    1. Granderson, Jessica & Price, Phillip N., 2014. "Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models," Energy, Elsevier, vol. 66(C), pages 981-990.
    2. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    3. Granderson, Jessica & Price, Phillip N. & Jump, David & Addy, Nathan & Sohn, Michael D., 2015. "Automated measurement and verification: Performance of public domain whole-building electric baseline models," Applied Energy, Elsevier, vol. 144(C), pages 106-113.
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    Cited by:

    1. Marian Kampik & Marcin Fice & Adam Pilśniak & Krzysztof Bodzek & Anna Piaskowy, 2023. "An Analysis of Energy Consumption in Small- and Medium-Sized Buildings," Energies, MDPI, vol. 16(3), pages 1-21, February.

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