| Author: | Fung, Tze-chiu |
| Title: | Short term load forecasting for system control & operational planning |
| Degree: | M.Sc. |
| Year: | 1997 |
| Subject: | Electric power-plants -- Load -- Forecasting Electric power-plants -- Load -- Mathematical models Neural networks (Computer science) Electric power consumption -- Forecasting Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies |
| Pages: | vii, 72 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | Short Term Load Forecasting is one of the principal elements in power system operation. It is required for unit commitment, energy transfer scheduling, load dispatch and security assessment. Accuracy in load forecasting is a crucial task in generation cost optimisation. This project presents an artificial neural network (ANN) based Short Term Load Forecasting Model. The model is developed to meet the needs and to cope with the environment of the supply utilities in Hong Kong. Four different categories of models (Weekday, Saturday, Sunday and Holiday) are proposed. Detailed study is carried out in terms of training and testing the neural nets with historical power utility data and weather data. A sufficient number of effective learning data are selected by taking advantage of the expert knowledge of the experienced utility engineers. The forecasting model consists of two part: 1) off line neural network models for learning and testing, and 2) on line models for engineers in daily load prediction. The developed model is applied to load forecasting of a power utility in Hong Kong for field test. As far as the objective of introducing automation to the load forecasting task is concerned, this project is considered a successful attempt. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b1437013x.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.45 MB | Adobe PDF | View/Open |
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