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- W2914505053 endingPage "393" @default.
- W2914505053 startingPage "393" @default.
- W2914505053 abstract "Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed." @default.
- W2914505053 created "2019-02-21" @default.
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- W2914505053 date "2019-01-27" @default.
- W2914505053 modified "2023-10-13" @default.
- W2914505053 title "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview" @default.
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- W2914505053 doi "https://doi.org/10.3390/en12030393" @default.
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