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Forecasting Research of Long-Term Solar Irradiance and Output Power for Photovoltaic Generation System

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3 Author(s)
Hang Cheng ; Dept. of Electr. Eng., Lanzhou Inst. of Technol., Lanzhou, China ; Wu-shun Cao ; Peng-jiang Ge

In this paper, the solar irradiance time series was classified by seasons, and the time series of each season were decomposed trend term and random term, the trend term was mainly influenced by geographical factors such as latitude, altitude, etc. While the random term largely reflects weather conditions. The trend term was fitted by least square method. Based on the time series theory, the random term forecasting results from established autoregressive moving average model (ARMA) model, the final forecasting results of the original solar irradiance is the superimposition of the respective prediction. It shows that the proposed model has a certain accuracy through compared the simulation results with the measured data from the certain region (29 degrees north latitude). Finally, the paper discusses the output power prediction model for PV generation system by the forecasting solar irradiance.

Published in:

Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on

Date of Conference:

17-19 Aug. 2012