1. Forecasting high resolution electricity demand data with additive models including smooth and jagged components
- Author
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Anestis Antoniadis, Umberto Amato, Yannig Goude, Italia De Feis, and Audrey Lagache
- Subjects
Flexibility (engineering) ,Mathematical optimization ,Electrical load ,Computer science ,Semi-parametric additive model ,05 social sciences ,Generalized additive model ,Short-term load forecasting ,Wavelets ,Random forest ,Smart grid ,Wavelet ,0502 economics and business ,050207 economics ,Business and International Management ,Penalised least-squares ,Additive model ,050205 econometrics ,Interpretability - Abstract
Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
- Published
- 2021