Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview

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.

Keywords

• short-term load forecasting
• demand-side management
• pattern similarity
• hierarchical short-term load forecasting
• feature selection
• weather station selection