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Short-term electric power load forecasting using feedforward neural networks

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This paper presents the results of a study on short-term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting.
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Keywords: feedforward neural network; gradient descent learning; short-term power load forecasting; weather conditions

Document Type: Research Article

Affiliations: 1: Engineering Technology Department, University of Houston, USA 2: Department of Electrical and Computer Engineering, University of Houston, USA

Publication date: July 1, 2004

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