On accuracy of demand forecasting and its extension to demand composition forecasting using artificial intelligence based methods

Accurate prediction of the load plays an indispensable role in power system planning and electricity market analysis. Load forecasting based on artificial intelligence (AI) techniques received a significant attention in the past and it is rapidly developing because of its high accuracy. Some of the AI based methodologies for load forecasting have already been adopted and widely used by the industry. This paper presents for the first time comparative analysis of the state of the art artificial neural network (ANN) based and adaptive neuro-based fuzzy inference system (ANFIS) based load forecasting methodologies in the same operation environment. Furthermore, the paper implements the extension of forecasting tools to forecast hourly load composition in addition to overall load at a bus. It is confirmed that either approach is very effective in load forecasting and that they have comparable performance providing appropriate setting of relevant parameters. It also proves that the approach can be successfully extended to hourly load composition forecasting and the load composition forecasting error is less than 10% at most of the time during the day.