Short-term electric load forecasting using empirical mode decomposition based optimized extreme learning machine

The electric load demand is thriving faster day-to-day which is highly nonlinear, complex and noisy in nature because of its partial dependency on weather condition (temperature, humidity, pressure and etc.). This makes the load prediction extremely difficult. In this work, the performance of Empirical Mode Decomposition (EMD) based optimized Extreme Learning Machine (OELM) is demonstrated for day ahead load forecasting of Chhattisgarh state of India. EMD method is used to disintegrate the nonlinear load data into some simple and stationary dataset by which the prediction competency of machine learning algorithm can be enhanced. Each decomposed dataset is predicted individually by dedicated OELM. OELM learning algorithm is established by optimizing the parameters of ELM (weights and biases) using Crow Search Algorithm (CSA). Further, the competency of OELM is improved by proposing Craziness based CSA (CCSA) with upgraded potential to equipoise between investigation and exploitation. The consolidation of EMD method and proposed CCSA is applied to achieve better efficacy in load demand prediction. The predicted demand of proposed predictive model is demonstrated by using performance measures and hypotheses tests. The superiority of proposed predictive model over recently published work is substantiated for load demand forecasting of three different cities of Australia. The simulation results contribute the indication that the proposed EMD CCSA OELM model can be taken as a better tool for load forecasting.