(untitled) (Journal of Science and Technology, 2016)
Monthly energy forecasts help heavy consumers of electric power to prepare adequate budget to pay their electricity bills and also draw the attention of management and stakeholders to electric- ity consumption levels so that energy efficiency measures are put in place to reduce cost. In this paper, a wavelet transform and radial basis function neural network based energy forecast model is developed to predict monthly energy consumption. The model was developed using the monthly energy consumption of Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana for a 9-year period. A mean absolute percentage error of 7.94% was achieved when the forecast model was tested over a 60-month period.