Intelligent Machine Learning With Evolutionary Algorithm Based Short Term Load Forecasting in Power Systems

Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load (STLF) plays vital part in regulated systems and electricity markets, which is commonly employed to predict the outcomes failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) involves different operations such as data decomposition, preprocessing, feature selection, prediction, parameter tuning. Wavelet transform (WT) used decomposition time series Oppositional Artificial Fish Swarm Optimization (OAFSA) selection technique elect optimal set features. In order improvise convergence rate AFSA, oppositional (OBL) concept integrated into it. Then, water wave optimization (WWO) Elman neural networks (ENN) model predictive process. Finally, inverse WT applied obtained hourly data. To validate effective outcome IMLEA-STLF extensive simulations take place on benchmark dataset. The resultant values ensured promising results over other compared methods.