SUMMARYRecently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electrical power and heat are controlled with a communication network. We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan. As a result of comparing measurements with prediction values, the average prediction error per day was about 26% of the measured power.

Keywords: photovoltaic power system, generated power forecasting, energy network Recently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electric power and heat are controlled with a communications network.Actual deployment of an economical and enviromentally friendly energy network requires optimal scheduling technology. Photovoltaic power system is greatly affected by climate and weather conditions. Environmental and economic benefits could be markedly improved if we could accuracy estimate the power of photovoltaic power system and then determine the operating schedule accordingly.We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. Projected temperture and weather conditions for the next day are obtained from the weather forecast.This imformation is then combined with past measured data to estimate the amount of power generation of photovoltaic power system. Fig. 1. Photovoltaic power systemWe carried out forecasting power output of the photovoltaic power system installed in Expo 2005 * , Aichi Japan, (Fig. 1, Fig. 2). As a result of comparing measurements with a prediction values, the average prediction error per day was about 26% of the measured power. -11 -

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