A hybrid monthly electricity demand forecasting model combining an Hodrick-Prescott filter, recurrent neural networks, and autoregressive integrated moving average

The coexistence of growth trends and seasonal fluctuations in monthly electricity demand presents significant forecasting challenges. Therefore, this study proposes a univariate time series forecasting approach that applies the Hodrick-Prescott (HP) filter to decompose the demand series into trend a...

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Hlavní autoři:	Zhenyu Su, Juan Zhang, Zhehan Yang, Leihao Ma
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2025-12-01
Edice:	Energy and AI
Témata:	Electricity demand forecasting Hodrick-prescott filter Recurrent neural networks Autoregressive integrated moving average model
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S2666546825001326
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