An implementation of a neural network based load forecasting model for the EMS

This paper presents the development and implementation of an artificial neural network (ANN) based short-term system load forecasting model for the energy control center of the Pacific Gas and Electric Company (PG&E). 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. Attention was paid to model accurately special events, such as holidays, heat-waves, cold snaps and other conditions that disturb the normal pattern of the load. The significant impact of special events on the model's performance was established through testing of an existing load forecasting package that is in production use. 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 the existing. regression based model 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. The ANN model has also been integrated with PG&E's Energy Management System (EMS). It is envisioned that the ANN model will eventually substitute the existing model to support the Company's real-time operations. In the interim both models will be available for production use.<
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