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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Charytoniuk, W. Chen, M.-S. |
| Copyright Year | 1969 |
| Abstract | In a deregulated, competitive power market, utilities tend to maintain their generation reserve close to the minimum required by an independent system operator. This creates a need for an accurate instantaneous-load forecast for the next several dozen minutes. This paper presents a novel approach to very short-time load forecasting by the application of artificial neural networks to model load dynamics. The proposed algorithm is more robust as compared to the traditional approach when actual loads are forecasted and used as input variables. It provides more reliable forecasts, especially when the weather conditions are different from those represented in the training data. The proposed method has been successfully implemented and used for online load forecasting in a power utility in the United States. To assure robust performance and training times acceptable for online use, the forecasting system was implemented as a set of parsimoniously designed neural networks. Each network was assigned a task of forecasting load for a particular time lead and for a certain period of day with a unique pattern in load dynamics. Some details of this are presented in the paper. |
| Sponsorship | IEEE Power Engineering Society |
| Starting Page | 263 |
| Ending Page | 268 |
| Page Count | 6 |
| File Size | 481159 |
| File Format | |
| ISSN | 08858950 |
| Volume Number | 15 |
| Issue Number | 1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-02-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Load forecasting Artificial neural networks Weather forecasting Robustness Power markets Power generation Economic forecasting Load modeling Predictive models Heuristic algorithms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Energy Engineering and Power Technology Electrical and Electronic Engineering |
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