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Predicting intra-day load profiles under time-of-use tariffs using smart meter data

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  • Y, Kiguchi
  • Y, Heo
  • M, Weeks
  • R, Choudhary

Abstract

The installation of smart meters enabling electricity load to be measured with half-hourly granularity provides an innovative demand-side management opportunity that is likely to be advantageous for both utility companies and customers. Time-of-use tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector. Although there exists a large body of research on demand response in electricity pricing, a practical framework to forecast user adaptation under different Time-of-use tariffs has not been fully developed. The novelty of this work is to provide the first top-down statistical modelling of residential customer demand response following the adoption of a Time-of-use tariff and report the model's accuracy and the feature importance. The importance of statistical moments to capture various lifestyle constraints based on smart meter data, which enables this model to be agnostic about household characteristics, is discussed. 646 households in Ireland during pre/post-intervention of Time-of-use tariff is used for validation. The value of Mean Absolute Percentage Error in forecasting average load for a group of households with the Random Forest method investigated is 2.05% for the weekday and 1.48% for the weekday peak time.

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  • Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    • Handle: RePEc:eee:energy:v:173:y:2019:i:c:p:959-970
    DOI: 10.1016/j.energy.2019.01.037
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    8. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

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