Determination Method of Insolation Prediction With Fuzzy and Applying Neural Network for Long-Term Ahead PV Power Output Correction

作者：Yona Atsushi^*; Senjyu Tomonobu; Funabashi Toshihisa; Kim Chul Hwan

来源：IEEE Transactions on Sustainable Energy, 2013, 4(2): 527-533.

DOI：10.1109/TSTE.2013.2246591

• Fuzzy theory
• hourly forecast errors
• neural network (NN)
• photovoltaic (PV) generated power forecasting
• weather reported data

In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and the output of a photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV systems as accurately as possible, an insolation estimation method is required. This paper proposes the power output forecasting of a PV system based on insolation forecasting at 24 hours ahead by using weather reported data, fuzzy theory, and neural network (NN). If the suitable training data is not selected, the training process of NN tends to be unstable. The proposed technique for application of NN is trained by power output data based on fuzzy theory and weather reported data. Since the fuzzy model determines the insolation forecast data, NN will train the power output smoothly. The validity of the proposed method is confirmed by comparing the forecasting abilities on the computer simulations.

• 出版日期2013-4