Electric Forecasting using Nature Inspired Optimization Techniques
Anamika Singh1, Manish Kumar Srivastava2, Navneet Kumar Singh3
1Anamika Singh* , Department of Electrical Engineering, Sam Higginbottom University of Agriculture, Technology And Sciences, Prayagraj, India.
2Manish Kumar Srivastava, Department of Electrical Engineering, Sam Higginbottom University of Agriculture, Technology And Sciences, Prayagraj, India.
3Navneet Kumar Singh, Department of Electrical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2259-2263 | Volume-8 Issue-6, August 2019. | Retrieval Number:F8639088619/2019©BEIESP | DOI: 10.35940/ijeat.F8639.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Artificial neural network, electric forecasting, genetic algorithm and particle swarm optimization.