Electric Forecasting using Nature Inspired Optimization Techniques

Abstract

Now a day, there exists huge competition among power industries in terms of fulfilling various customers' electrical needs. Reliable and quality power supply is no doubt a basic need for all power consumers. Moreover, planning & operation engineers also targets for proper unit commitment, economic power dispatch, etc., and highly depend upon good power system planning. Therefore, Electric Forecasting (EF) is a major criterion for power engineers. In this manuscript, Artificial Neural Network (ANN), being a well established tool for modeling non-linear and black box systems, is used to forecast hydro generation power plant, energy met and peak demand of India.Furthermore, in this competitive world, ANN model is further optimized using genetic algorithm (GA) and particle swarm optimization (PSO) to explore accurate forecasting model with minimal amount of error. These optimization methods explore highly diversified search area, resulting in more accurate forecasting results in comparison to ANN when trained with standard back propagation training algorithm. India. His research area includes power system planning, transformer, energy efficiency, load forecasting, neural network. Navneet Kumar Singh received the PhD degree from Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India. Currently he is with Electrical Engineering Department, MNNIT Allahabad, India. His research area includes load forecasting, power system planning and Application of Artificial Intelligence (AI) techniques in power system.