Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80670
Title: Computational intelligence on short-term load forecasting : a methodological overview
Authors: Fallah, SN
Ganjkhani, M
Shamshirband, S
Chau, KW 
Keywords: Demand-side management
Feature selection
Hierarchical short-term load forecasting
Pattern similarity
Short-term load forecasting
Weather station selection
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Energies, 2019, v. 12, no. 3, 393 How to cite?
Journal: Energies 
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.
URI: http://hdl.handle.net/10397/80670
EISSN: 1996-1073
DOI: 10.3390/en12030393
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Fallah SN, Ganjkhani M, Shamshirband S, Chau K-W. Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview. Energies. 2019; 12(3):393 is available at https://doi.org/10.3390/en12030393
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