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Authors:
Emmanuel Asuming Frimpong
Philip Yaw Okyere

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Abstract:
Load forecasting helps electricity utilities make important decisions such as purchasing and generating electric power, load switching and infrastructure development. Energy suppliers, financial institutions and other participants in electric energy generation, transmission, distribution and markets benefit from load forecasts. This paper proposes the combination of wavelet analysis and radial basis function (RBF) neural network as tools for STLF. The forecasting model developed predicts peak load one day ahead. The model was developed using a year