DocumentCode :
1194959
Title :
An implementation of a neural network based load forecasting model for the EMS
Author :
Papalexopoulos, Alex D. ; Hao, Shangyou ; Peng, Tie-Mao
Author_Institution :
Pacific Gas & Electr. Co., San Francisco, CA, USA
Volume :
9
Issue :
4
fYear :
1994
fDate :
11/1/1994 12:00:00 AM
Firstpage :
1956
Lastpage :
1962
Abstract :
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
Keywords :
learning (artificial intelligence); load forecasting; load management; neural nets; power system control; statistical analysis; EMS; Energy Management System; Pacific Gas and Electric Company; average errors; cold snaps; energy control center; heat-waves; holidays; load forecasting model; neural network; power system; real-time operations; regression based model; short-term system load forecasting model; testing; training; Artificial neural networks; Electric variables control; Input variables; Load forecasting; Load modeling; Neural networks; Packaging; Predictive models; Production; Testing;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/59.331456
Filename :
331456
Link To Document :
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