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• W3194465593 startingPage "107477" @default.
• W3194465593 abstract "To keep the balance between electricity demand and supply as well as infrastructure planning, it is important to accurately forecast the electricity demand. This has become a challenging task due to increasing share of renewable energy and prosumers (i.e. consumers who produce electricity) in the electricity grid. This paper develops a cooperative ensemble framework which divides the forecasting problem into several subtasks based on peak and off-peak conditions. Each subtask is then solved using multiple forecasting models that include classification and regression. The developed framework is finally validated on real-world operational demand across the National Electricity Market (NEM) of Australia. The performance is comprehensively compared against various state-of-the-art techniques in the field, which indicates up to 25.4% mean absolute error (MAE) improvement." @default.
• W3194465593 created "2021-08-30" @default.
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• W3194465593 date "2021-12-01" @default.
• W3194465593 modified "2023-10-09" @default.
• W3194465593 title "Machine learning based novel ensemble learning framework for electricity operational forecasting" @default.
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• W3194465593 doi "https://doi.org/10.1016/j.epsr.2021.107477" @default.
• W3194465593 hasPublicationYear "2021" @default.
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