Hourly load forecasting using Artificial Neural Network for a small area

Accurate load forecasting play a key role in economical use of energy and real time security analysis of system. Artificial Neural Network (ANN) model have been extensively implemented to produce accurate results for short-term load forecasting with time lead ranging from an hour to a week. In this paper a practical case of the small load area of a town getting supplied by 19 distribution feeders is considered with dominant residential-type of load. Historical load and temperature data is collected from January-2010 to December-2010. Four weather seasons are defined by the Meteorological Department, India. Each season includes the group of month. Representative months are selected from each season by observing the variation in load behavior patterns. An input vector composed of load and temperature values at previous instants, is employed to train ANN designed for each selected month by using Back-Propagation algorithm with Momentum learning rule. ANN testing is carried out and their performance is evaluated using mean absolute percentage error (MAPE) criterion. Finally, error values are compared for each month and hence the deviation in forecasting ability of ANN is observed for each month and season.