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Short-term system load forecasting using an artificial neural network

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3 Author(s)
Papalexopoulos, A.D. ; Pacific Gas & Electric Co., San Francisco, CA, USA ; Shangyou Hao ; Peng, T.-M.

This paper presents a new, artificial neural network (ANN) based model for the calculation of next day's load forecasts. The model's most significant aspects fall into the following two areas: training process and selection of the input variables. Insights gained during the development of the model regarding the choice of the input variables, and their transformations, the design of the ANN structure, the selection of the training cases and the training process itself are described in the paper. The new model has been tested under a wide variety of conditions and it is shown in this paper to produce excellent results. Comparison results between an existing regression-based model that is currently in production use and the ANN model are very encouraging. The ANN model consistently outperforms the existing model in terms of both average errors over a long period of time and number of 'large' errors. Conclusions reached from this development are sufficiently general to be used by other electric power utilities.

Published in:

Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of

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