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Computational intelligence on short-term load forecasting: a methodological overview

Fallah, N ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.3390/en12030393
  3. Publisher: MDPI AG , 2019
  4. Abstract:
  5. 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. © 2019 by the authors
  6. Keywords:
  7. Demand-side management ; Feature selection ; Hierarchical short-term load forecasting ; Short-term load forecasting ; Weather station selection ; Artificial intelligence ; Demand side management ; Electric power plant loads ; Electric utilities ; Feature extraction ; Computational intelligence techniques ; Electricity demand forecasting ; Energy fields ; Hybrid strategies ; Pattern similarity ; Power system scheduling ; Short term load forecasting ; Weather stations ; Weather forecasting
  8. Source: Energies ; Volume 12, Issue 3 , 2019 ; 19961073 (ISSN)
  9. URL: https://www.mdpi.com/1996-1073/12/3/393