Electricity demand modeling using hybrid optimization of genetic algorithm and particle swarm optimization in artificial neural network

Abstract

Demand forecasting plays a dominant part in the economic optimization and secure operation of electric power systems long term load forecasting represents the first step in developing future generation transmission and distribution facilities any substantial deviation in the forecast particularly under the new market structure will result in either overbuilding of supply facilities or curtailment of customer demand the confidence levels associated with classical forecasting techniques when applied to forecasting problem in mature and stable utilities are unlikely to be similar to those of dynamic and fast growing utilities this is attributed to the differences in the nature of growth socio economic conditions occurrence of special events extreme climatic conditions and the competition in generation due to the deregulation of the electricity sector with possible changes in tariff structures under such conditions these forecasting techniques are insufficient to establish demand forecast for long term power system planning consequently this case requires separate consideration either by pursuing the search for more improvement in the existing forecasting techniques or establishing another approach to address the forecasting problem of such systems newline