| Author: | Chan, Chun-keung |
| Title: | Neural network to area short-term load forecasting |
| Degree: | M.Sc. |
| Year: | 1997 |
| Subject: | Neural networks (Computer science) Electric power systems -- Load dispatching -- Data processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Electrical Engineering |
| Pages: | 51 leaves : ill. ; 31 cm |
| Language: | English |
| Abstract: | Area load forecasting plays one of the most critical roles in power system control centers. The accuracy of forecasting influences of the decision-making in overall unit commitment, and fuel allocation and off-line network analysis. Accurate system load forecasting is a potential source of great savings for electric utilities. It is difficult to model the relationships between system load and factors that influence it. Another difficulty lies in estimating and adjusting the model parameters. These parameters are estimated from historical data and they may become obsolete or may not be able to reflect load pattern changes for short time periods. In addition, load forecasting models rely heavily on the particular utilities environment in which the models are developed, therefore, they are not sufficiently general to be transferred easily from one company to another. Neural network theory is a potentially powerful technique for solving the above problems. Multiple-layer feedforward module is investigated for this application. The concepts and methodology formulated have been tested by using load and weather data from power utilities. The testing shows that the proposed approaches have superior qualities in dealing with difficulties encountered in the traditional techniques. In addition, the numerical simulation results show that this approach may provide accurate forecast. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14732531.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.86 MB | Adobe PDF | View/Open |
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