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Application of system identification modelling to solar hybrid systems for predicting radiation, temperature and load

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  • Sinha, S
  • Kumar, Sanjay
  • Matsumoto, Tsuyoshi
  • Kojima, Toshinori

Abstract

Uncertainties in local solar radiation, ambient temperature and thermal load data have been one of the major factors limiting the reliability and efficiency of solar thermal hybrid systems. In the present paper, moving average auto regressive exogenous (ARX) model based reasoning has been mooted and modified to include moving average method, as an effective tool for predictions of these data. The results show that the method is quite robust and is capable of predicting fairly accurate results, which would make these systems more viable in areas where meteorological data are not available or vague.

Suggested Citation

  • Sinha, S & Kumar, Sanjay & Matsumoto, Tsuyoshi & Kojima, Toshinori, 2001. "Application of system identification modelling to solar hybrid systems for predicting radiation, temperature and load," Renewable Energy, Elsevier, vol. 22(1), pages 281-286.
    • Handle: RePEc:eee:renene:v:22:y:2001:i:1:p:281-286
    DOI: 10.1016/S0960-1481(00)00034-3
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    Cited by:

    1. Chu, Yinghao & Li, Mengying & Coimbra, Carlos F.M., 2016. "Sun-tracking imaging system for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 96(PA), pages 792-799.
    2. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Real-time prediction intervals for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 83(C), pages 234-244.

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