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
Cite this paper
A. Yona, T. Senjyu, T. Funabashi, P. Mandal and C. Kim, "Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System," Smart Grid and Renewable Energy, Vol. 4 No. 6, 2013, pp. 32-38. doi: 10.4236/sgre.2013.46A004.
 P. D. Wasserman, “Neural Computing: Theory and Practice,” Van Nostrand Reinhold, New York, 1989.
 T. Hiyama and K. Kitabayashi, “Neural Network Based Estimation of Maximum Powergeneration from PV Module Using Environmental Information,” IEEE Transaction on Energy Conversion, Vol. 12, No. 3, 1997, pp. 241-247. doi:10.1109/60.629709
 A. Sozen, E. Arcakhoglu, M. Ozalp and N. Caglar, “Forecasting Based on Neural Network Approach of Solar Potential in Turkey,” Renewable Energy, Vol. 30, No. 7, 2005, pp. 1075-1090. doi:10.1016/j.renene.2004.09.020
 H. K. Elminir, F. F. Areed and T. S. Elsayed, “Estimation of Solar Radiation Components Incident on Helwansite Using Neural Networks,” Solar Energy, Vol. 79, No. 3, 2005, pp. 270-279. doi:10.1016/j.solener.2004.11.006
 K. S. Reddy and M. Ranjan, “Solar Resource Estimation Using Artificial Neural Networks and Comparison with Other Correlation Models,” Energy Conversion and Management, Vol. 44, No. 15, 2003, pp. 2519-2530.
 M. Mohandes, A. Balghonaim, M. Kassas,S. Rehman and T. O. Halawani, “Use of Functions for Estimating Monthly Solar Radiation,” Solar Energy, Vol. 68, No. 2, 2000, pp. 161-168. doi:10.1016/S0038-092X(99)00071-7
 Y. Kitamura and A. Matsuda, “Study on Raising Efficiency of Heat Accumulating Air Conditioning System Using Knowledge Precessing Techniques,” Journal of Mitsubishi Research Institute, No. 36, 2000, pp. 31-51.
 J. F. Elman, “Finding Structure in Time,” Cognitive Science, Vol. 14, No. 2, 1990, pp. 179-211.
 B. Kermanshahi, “Recurrent Neural Network for Forecasting Next 10 Years Loads of Nine Japanese Utilities,” Neurocomputing, Vol. 23, No. 1-3, 1998, pp. 125-133.
 M. T. Hagan and M. B.Menhaj, “Training Feed-Forward Networks with the Marquardt Algorithm,” IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1994, pp. 989993. doi:10.1109/72.329697
 M. Santamouris, G. Mihalakakou, B. Psiloglou, G. Eftaxias and D. N. Asimakopoulos, “Modeling the Global Solar Radiation on the Earth’s Surface Using Atmospheric Deterministic and Intelligent Data-Driven Techniques,” American Meteorological Society, Vol. 12, No. 10, 1999, pp. 179-211.
 AmdView(C) Weather Toy, “Meteorological Data Base: Ground Observation Ver. 1.00,” Japan Meteorological Business Support Center, Tokyo, 2004.
 A. Iga and Y. Ishihara, “Characteristics and Embodiment of the Practical Use Method of ‘Monthly Temperature Coefficient’ of the Photovoltaic Generation System,” IEEE Japan Transactions on Power and Energy, Vol. 126, No. 8, 2006, pp. 767-775.