One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities
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DOI: 10.1016/j.energy.2021.120966
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Cited by:
- Chen, Yunxiao & Lin, Chaojing & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2024. "Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load," Energy, Elsevier, vol. 302(C).
- Oscar Trull & J. Carlos Garc'ia-D'iaz & Angel Peir'o-Signes, 2024. "mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters," Papers 2402.10982, arXiv.org.
- Trull, Oscar & García-Díaz, J. Carlos & Peiró-Signes, A., 2022. "Multiple seasonal STL decomposition with discrete-interval moving seasonalities," Applied Mathematics and Computation, Elsevier, vol. 433(C).
- Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
- Hu, Wenxuan & Scholz, Yvonne & Yeligeti, Madhura & Deng, Ying & Jochem, Patrick, 2024. "Future electricity demand for Europe: Unraveling the dynamics of the Temperature Response Function," Applied Energy, Elsevier, vol. 368(C).
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Keywords
Time series; Forecasting; Electricity demand; Anomalous load;All these keywords.
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