Computational Intelligence Load Forecasting: A Methodological Overview

Abstract

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.

Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

“…Anirudh (2020) employed SIR, SEIRU, SEIR, SLIAR, SIRD, ARIMA and SIDARTHE for the COVID-19 pandemic prediction [5]. But, mathematical models have disadvantages such as complexity, time-consuming and lower reliability [6, 7].…”

Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer  

“…Anirudh (2020) employed SIR, SEIRU, SEIR, SLIAR, SIRD, ARIMA and SIDARTHE for the COVID-19 pandemic prediction [5]. But, mathematical models have disadvantages such as complexity, time-consuming and lower reliability [6,7].…”