Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network

In this paper, novel artificial neural network (ANN) based weather-load and weather-energy models have been developed to forecast electric load and energy for 24 months ahead. A set of weather and other variables which have been identified for both models together with their correlations and contribution to the forecasted variable is reported. The proposed ANN models have been applied to historical energy, load, and weather data available for the Muscat power system from 1986 to 1990. Forecast results, when compared with the actual data for 1991–1992, show that monthly electric energy and load can be predicted within a maximum error of 6% and 10%, respectively, even with forecasted weather. The proposed ANN models provide better accuracy than previously developed models.