NDLI: Short-term system load forecasting using an artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Papalexopoulos, A.D. Hao, S. Peng, T.-M.
Copyright Year	1993
Description	Author affiliation: Pacific Gas & Electric Co., San Francisco, CA, USA (Papalexopoulos, A.D.; Hao, S.)
Abstract	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.
Starting Page	239
Ending Page	244
File Size	733070
Page Count	6
File Format	PDF
ISBN	0780312171
DOI	10.1109/ANN.1993.264284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-04-19
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Artificial neural networks Autoregressive processes Economic forecasting Load modeling Predictive models Input variables Power system security Shape Power system modeling
Content Type	Text
Resource Type	Article

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