Development of a Solar Forecasting Model based on Ground Data in Wide Area Using Neural Networks

Abstract

This paper is concerned with the short-term forecasting of the time variation of the total output of photovoltaics over a wide area. Thermal power generation is essential for adjusting unstable output fluctuations of photovoltaic power output. Meanwhile, thermal power plants are forced to operate in no-load standby mode to cope with unforecastable power fluctuations. To reduce wasteful standby operation, we developed a new forecasting techniques using a neural network to forecast short-term changes in the total output of wide-area solar power. We applied the developed model to the northern part of Kyushu in Japan and forecasted solar power output 20 minutes ahead. It was evaluated by the amount of solar radiation itself and the clear-sky index. The forecasting reproduced the actual data well, especially when the clear-sky index was applied.