Grid reliability enhancement by peak load forecasting with a PSO hybridized ANN model

Raised complexity levels of conventional grids, ever increasing load demands, elevated reliability issues, limitations of conventional power generating units and quality issues associated with sustainable energy resources all highlight the need to adapt specific demand management techniques to augment grid reliability. The stability of grids is hampered by varying loads, intermittent supply of the renewable resources and the inability of conventional plants to deal with them making the grids extremely vulnerable during peak load times. In this study we propose the utilization of a hybrid load forecasting tool based on Artificial Neural Network and Particle Swarm Optimization (PSO) as the solution. The tool would forecast futuristic load and would be specifically effective for the peak load times enabling utilities to optimize grid performance.