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Improved multiple linear regression based models for solar collectors

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  • Kicsiny, Richárd

Abstract

Mathematical modelling is the theoretically established tool to investigate and develop solar thermal collectors as environmentally friendly technological heat producers. In the present paper, the recent and accurate multiple linear regression (MLR) based collector model in Ref. [1] is empirically improved to minimize the modelling error. Two new, improved models called IMLR model and MPR model (where MPR is the abbreviation of multiple polynomial regression) are validated and compared with the former model (MLR model) based on measured data of a real collector field. The IMLR and the MPR models are significantly more precise while retaining simple usability and low computational demand. Many attempts to decrease the modelling error further show that the gained precision of the IMLR model cannot be significantly improved any more if the regression functions are linear in terms of the input variables. In the MPR model, some of the regression functions are nonlinear (polynomial) in terms of the input variables.

Suggested Citation

  • Kicsiny, Richárd, 2016. "Improved multiple linear regression based models for solar collectors," Renewable Energy, Elsevier, vol. 91(C), pages 224-232.
    • Handle: RePEc:eee:renene:v:91:y:2016:i:c:p:224-232
    DOI: 10.1016/j.renene.2016.01.056
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    References listed on IDEAS

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    1. Kicsiny, Richárd, 2015. "Transfer functions of solar heating systems for dynamic analysis and control design," Renewable Energy, Elsevier, vol. 77(C), pages 64-78.
    2. Deng, Jie & Xu, Yupeng & Yang, Xudong, 2015. "A dynamic thermal performance model for flat-plate solar collectors based on the thermal inertia correction of the steady-state test method," Renewable Energy, Elsevier, vol. 76(C), pages 679-686.
    3. Buonomano, A. & Calise, F. & Palombo, A., 2013. "Solar heating and cooling systems by CPVT and ET solar collectors: A novel transient simulation model," Applied Energy, Elsevier, vol. 103(C), pages 588-606.
    4. Li, Yu-Chu Maxwell & Lu, Shyi-Min, 2005. "Uncertainty evaluation of a solar collector testing system in accordance with ISO 9806-1," Energy, Elsevier, vol. 30(13), pages 2447-2452.
    5. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
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    Cited by:

    1. Fan, Man & Liang, Hongbo & You, Shijun & Zhang, Huan & Yin, Baoquan & Wu, Xiaoting, 2018. "Applicability analysis of the solar heating system with parabolic trough solar collectors in different regions of China," Applied Energy, Elsevier, vol. 221(C), pages 100-111.
    2. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
    3. Kicsiny, Richárd, 2018. "Black-box model for solar storage tanks based on multiple linear regression," Renewable Energy, Elsevier, vol. 125(C), pages 857-865.

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