Abstract
In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other method
Highlights
Solar energy is well-known as clean energy because of no carbon dioxide emission
In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction
The validity of the proposed Recurrent Neural Network (RNN) is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other method
Summary
Solar energy is well-known as clean energy because of no carbon dioxide emission. photo-voltaic (PV) systems are rapidly gaining acceptance as one of the best solutions for the alternative energy source. Amount of storage battery energy is decided by forecasting data These decisions are benefits for effective running of hybrid power systems and their profitability depends on the forecast technique. Meteorological Agency or weather service will provide forecasting data free of charge, the implementation of above-mentioned techniques results in higher cost Because, these data are forecasting data of a wide area mostly, and are difficult for getting to know the exact value of the place in which the PV system is installed. The proposed technique for application of RNN is trained by only historical data of solar radiation and tested for the target term These data are observed only one site, and the type of RNN used is Elman network [7,8,9]. The validity of the proposed RNN is confirmed by comparing the forecasting abilities of FNN and RNN on the computer simulations at several-hour-ahead
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