Solar Irradiance Forecasting Using Intelligent Technology
Abstract
Because solar power is susceptible to clouds and substances in the air, the solar photovoltaic cannot produce stable power output. Solar irra diance is a measurement of t he power output of photovoltaic module . Therefore, this paper uses some different combination inputs of the neural network to develop the solar irradiance forecasting with 24 hours ahead. Their forecasting performances are evaluated and some comparison results in Taichung solar farm are given .
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Introduction
To solve the problems of limited fossil fuels and their impact on the environment, renewable resources play an impor tant role. Solar energy is a very important renewable energy. Based on evaluated condition of solar power, solar photovoltaic becomes the most potential renewable energy in Taiwan. Solar irradiance is a measurement of the power output of photovoltaic modul e. However, because solar irradiance is influenced by substances in the air, the solar photovoltaic cannot produce stable power output. The power output of photovoltaic module is influenced immediately when the module is sheltered from the clouds. Besides, the material of solar cell, air temperature, module’s position and orientation also affect the power output of the pho tovoltaic module. Therefore, it i s an important issue to forecast solar irradiance accurately.
Forecasting accuracy is not only influence d by the change of weather but also surroundings and the effectiveness of method and data. Developing an excellent solar power systems not only can wield the change of solar power but also help power company to allocate power. The accuracy of solar irradia nce forecasting is the b asis of solar power forecasting [1-6]. There are many intelligent approaches to forecast solar irradiance , such as neural networks [7 -13].
The paper uses neural network technology to develop the solar power forecasting with 1 -24 hou rs ahead. Some different features for solar forecasting are proposed and their forecasting performances are evaluated. Moreover, comparison results in Taichung solar farm in Taiwan [14] are given.
Conclusion
This paper has proposed s ome different features for solar irradiance forecasting and presented some comparison results of solar irradiance forecasting in Taichung solar farm . According to the RMSE comparison figures, the method depend on historical and future solar irradiance values is better than other methods in forecasting with 1-24 hours ahead. Moreover, the results are like ly to be affected by the historical data in the forecasting with short -time ahead .
