Fallah, S.N.; Ganjkhani, M.; Shamshirband, S.; Chau, K.-W. Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview. Energies2019, 12, 393.
Fallah, S.N.; Ganjkhani, M.; Shamshirband, S.; Chau, K.-W. Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview. Energies 2019, 12, 393.
Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.
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