Online 24-h solar power forecasting based on weather type classification using artificial neural network |
| |
Authors: | Changsong Chen Shanxu Duan Tao Cai Bangyin Liu |
| |
Affiliation: | State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China |
| |
Abstract: | Power forecasting is an important factor for planning the operations of photovoltaic (PV) system. This paper presents an advanced statistical method for solar power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of solar irradiance, relative humidity and temperature at the site of the photovoltaic power system. A self-organized map (SOM) is trained to classify the local weather type of 24 h ahead provided by the online meteorological services. A unique feature of the method is that following a preliminary weather type classification, the neural networks can be well trained to improve the forecast accuracy. The proposed method is suitable for operational planning of transmission system operator, i.e. forecasting horizon of 24 h ahead and for PV power system operators trading in electricity markets. Application of the forecasting method on the power production of an actual PV power system shows the validity of the method. |
| |
Keywords: | Power forecasting Solar power Neural network Weather type Photovoltaic power system |
本文献已被 ScienceDirect 等数据库收录! |