| Author: | Huang, Chen |
| Title: | Wind power forecasting |
| Degree: | M.Sc. |
| Year: | 2012 |
| Subject: | Wind power -- Forecasting. Wind power plants -- Forecasting. Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Engineering |
| Pages: | viii, 67 leaves : col. ill. ; 30 cm. |
| Language: | English |
| Abstract: | As a kind of renewable, clean, abundant and green energy, wind power technology has developed rapidly in recent years. Due to the low production cost and large energy resources available, it has become one of the new main power supplies in the world, and also is an important development trend of the renewable energy strategy. Despite the obvious benefits, wind power also has the shortcomings of intermittency and volatility because of the uncertainty of the wind. Hence, the forecasting of the wind power is essential to maintain the stability of power system. In this dissertation, it will firstly introduce the current wind power forecasting technology, including forecasting methods, models, some evaluation criteria and interval prediction theory. Prediction interval is considered more advanced for it can provide the reliability and certainty of the prediction results, comparing with the point prediction. In the simulation part, two widely-used forecasting models, ARMA model and artificial neural networks are chosen for short term (in hours) wind speed prediction. The ARMA model can only provide point forecasting, while neural networks provide both point and interval forecasting by using the bootstrap method. Also the prediction results of the two models are compared in the last part. Conclusions and future scope of the research work are described in the end. The wind speed data is provided by my supervisor for the simulations. All the data processing and model simulations will be conducted on MATLAB and its toolboxes. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b26492994.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.37 MB | Adobe PDF | View/Open |
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