期刊
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 48, 期 3, 页码 1064-1069出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2012.2190816
关键词
Forecasting; photovoltaic cell radiation effects; photovoltaic systems; support vector machine (SVM); weather classification
资金
- National Natural Science Foundation of China [50976034]
- Fundamental Research Funds for the Central Universities [09MG17]
- National High Technology Research and Development Program 863: Wind Power Prediction Method Study and System Development [2007AA05Z428]
Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.
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