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Comparing peak electricity load forecasting models for an industrial and a residential building

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  • Wood, Michael
  • Matrone, Silvana
  • Ogliari, Emanuele
  • Leva, Sonia

Abstract

Electrical load forecasting models are becoming more and more essential for energy savings and effective energy management and the ability to forecast the load peak is a crucial feature for many applications. This paper presents a comparative analysis of various methods with low computational complexity, including Naïve Persistence, Statistical load forecasting, Seasonal Auto-Regressive Integrated Moving Average with eXogenous regressors, and Long Short-Term Memory enhanced by Empirical Mode Decomposition pre-processing. They are tested to forecast daily peak electricity load and peak hour in two distinct existing scenarios: residential and industrial.

Suggested Citation

  • Wood, Michael & Matrone, Silvana & Ogliari, Emanuele & Leva, Sonia, 2026. "Comparing peak electricity load forecasting models for an industrial and a residential building," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 240(C), pages 303-316.
    • Handle: RePEc:eee:matcom:v:240:y:2026:i:c:p:303-316
    DOI: 10.1016/j.matcom.2025.06.029
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    References listed on IDEAS

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