Study of daily solar Irradiance forecast based on chaos optimization neural networks
Affiliation(s)
College of Urban Construction & Environment Engineering, University of Shanghai for Sci. & Tec., Box 484, 516 Jun Gong Rd., Shanghai 200093, China, Tel.: +86 21 55270275, Fax: +86 21 55270686;. College of Environmental Science & Engineering, Donghua University, Shanghai 201620, China.
College of Urban Construction & Environment Engineering, University of Shanghai for Sci. & Tec., Box 484, 516 Jun Gong Rd., Shanghai 200093, China, Tel.: +86 21 55270275, Fax: +86 21 55270686;. College of Environmental Science & Engineering, Donghua University, Shanghai 201620, China.
ABSTRACT
In this works, artificial neural network is com-bined with wavelet analysis for the forecast of solar irradiance. This method is characteristic of the preprocessing of sample data using wavelet transformation for the forecast, i.e., the data se-quence of solar irradiance as the sample is first mapped into several time-frequency domains, and then a chaos optimization neural network is established for each domain. The forecasted so-lar irradiance is exactly the algebraic sum of all the forecasted components obtained by the re-spective networks, which correspond respec-tively the time-frequency domains. On the basis of combination of chaos optimization neural network and wavelet analysis, a model is devel-oped for more accurate forecasts of solar irradi-ance. An example of the forecast of daily solar irradiance is presented in the paper, the historical daily records of solar irradiance in Shanghai constituting the data sample. The results of the example show that the accuracy of the method is more satisfacto
In this works, artificial neural network is com-bined with wavelet analysis for the forecast of solar irradiance. This method is characteristic of the preprocessing of sample data using wavelet transformation for the forecast, i.e., the data se-quence of solar irradiance as the sample is first mapped into several time-frequency domains, and then a chaos optimization neural network is established for each domain. The forecasted so-lar irradiance is exactly the algebraic sum of all the forecasted components obtained by the re-spective networks, which correspond respec-tively the time-frequency domains. On the basis of combination of chaos optimization neural network and wavelet analysis, a model is devel-oped for more accurate forecasts of solar irradi-ance. An example of the forecast of daily solar irradiance is presented in the paper, the historical daily records of solar irradiance in Shanghai constituting the data sample. The results of the example show that the accuracy of the method is more satisfacto
KEYWORDS
Daily Solar Irradiance Forecast; Wavelet Transformation; Chaos Optimization Neural Networks
Daily Solar Irradiance Forecast; Wavelet Transformation; Chaos Optimization Neural Networks
Cite this paper
Cao, S. , Chen, J. , Weng, W. and Cao, J. (2009) Study of daily solar Irradiance forecast based on chaos optimization neural networks. Natural Science, 1, 30-36. doi: 10.4236/ns.2009.11006.
Cao, S. , Chen, J. , Weng, W. and Cao, J. (2009) Study of daily solar Irradiance forecast based on chaos optimization neural networks. Natural Science, 1, 30-36. doi: 10.4236/ns.2009.11006.
References
[1] Woyte, A., Belmans, R. and Nijs, J. (2003) Analysing short-time irradiance fluctions by their characteristic time scales. Proc. of 3rd World Conf. on Photovoltaic Energy Conversion, 2290-2293. [2] Ortiz, R. E. I. and Peng F. Z. (2006) Algorithms to esti-mate the temperature and effective irradiance level over a photovoltaic module using the fixed point theorem. Power Electronics Specialists Conference, PESC’06, 37th IEEE, 1–4. [3] Li, J. and Song, A. G. (1998) Comparison of clear-day solar radiation model in Beijing to ASHRAE model. Journal of Capital Normal University, 19(1), pp. 35-38. [4] Ren, M. J. and Wright, J. A. (2002) Adaptive diurnal pre-diction of ambient dry-bulb temperature and solar radia-tion. HVAC and Research, 8(4), 383-401. [5] Zhang, S. N. and Tian, S. Y. (2007) Setup of the hourly solar irradiance model. Journal of Solar Energy, 18(3), 273-277, 1997. [6] Kalogirou, S. A. (2001) Artificial neural networks in re-newable energy systems applications: A review. Renewable and Sustainable Energy Reviews, 5(4), 373-401, 2001. [7] Rioul, O. and Vetterli, M. (1991) Wavelets and signal processing. IEEE SP Magazine, 8(4), 14-38. [8] Chui, C. K. (1992) An introduction to wavelets. Aca-demic Press, New York. [9] Debnath, L. (2002) Wavelet transforms and their applica-tions. Springer-Verlag Inc., New York. [10] Chen, L. and Aihara K. (1995) Chaotic simulated an-nealing by a neural network model with transient chaos. Neural Networks, 8(6), 915-930.
[1] Woyte, A., Belmans, R. and Nijs, J. (2003) Analysing short-time irradiance fluctions by their characteristic time scales. Proc. of 3rd World Conf. on Photovoltaic Energy Conversion, 2290-2293. [2] Ortiz, R. E. I. and Peng F. Z. (2006) Algorithms to esti-mate the temperature and effective irradiance level over a photovoltaic module using the fixed point theorem. Power Electronics Specialists Conference, PESC’06, 37th IEEE, 1–4. [3] Li, J. and Song, A. G. (1998) Comparison of clear-day solar radiation model in Beijing to ASHRAE model. Journal of Capital Normal University, 19(1), pp. 35-38. [4] Ren, M. J. and Wright, J. A. (2002) Adaptive diurnal pre-diction of ambient dry-bulb temperature and solar radia-tion. HVAC and Research, 8(4), 383-401. [5] Zhang, S. N. and Tian, S. Y. (2007) Setup of the hourly solar irradiance model. Journal of Solar Energy, 18(3), 273-277, 1997. [6] Kalogirou, S. A. (2001) Artificial neural networks in re-newable energy systems applications: A review. Renewable and Sustainable Energy Reviews, 5(4), 373-401, 2001. [7] Rioul, O. and Vetterli, M. (1991) Wavelets and signal processing. IEEE SP Magazine, 8(4), 14-38. [8] Chui, C. K. (1992) An introduction to wavelets. Aca-demic Press, New York. [9] Debnath, L. (2002) Wavelet transforms and their applica-tions. Springer-Verlag Inc., New York. [10] Chen, L. and Aihara K. (1995) Chaotic simulated an-nealing by a neural network model with transient chaos. Neural Networks, 8(6), 915-930.