Output Power Forecasting for 6kW Thin-Film PV System using Response Surface Methodology

Authors

  • Wenny Rumy Upkli Centre for Electrical, Robotic and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
  • Azhan Ab Rahman Fakulti Teknologi Kejuruteraan Elektrik & Elektronik, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
  • Intan Azmira Wan Abdul Razak Centre for Electrical, Robotic and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
  • Hairol Nizam Mohd Shah Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
  • Mohd Shahrieel Mohd Aras Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia

Keywords:

ambient temperature, module temperature, irradiance, photovoltaic (PV), RSM, monocrystalline, HIT, thin film

Abstract

Photovoltaic (PV) system is an attractive option for the energy sector nowadays due to its renewable nature. However, because of the unpredictable nature of weather, it is difficult to determine the generation of the PV system beforehand. Thus, forecasting is essential for the determination of Return of Investment (ROI) of a newly installed PV system. This paper proposes the application of Response Surface Methodology (RSM) to forecast the output power of the 6kW thin-film PV System. Three environmental elements are used; irradiance, module temperature, and ambient temperature. For that, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interactions, pure quadratic, and full quadratic was used. The 5 minutes sampling size data weather station from the year 2014 of the 3-phase three environmental elements and output power of 6kW thin-film was recorded and used. Whereas, yearly 2015 data of the aforementioned elements were used for validation. Forecasting performance measures such as the determination of coefficient (R2) method and root mean square error (RMSE) approach are presented. The results indicated that a full quadratic model provides the best forecasting model with a resulting R2 value of 0.9981 and gives the least amount of RMSE which is 18.74.

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Published

2020-12-31

How to Cite

Upkli, W. R. ., Ab Rahman, A. ., Wan Abdul Razak, I. A. ., Mohd Shah, H. N. ., & Mohd Aras, . M. S. . (2020). Output Power Forecasting for 6kW Thin-Film PV System using Response Surface Methodology. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 68(1), 143–162. Retrieved from https://www.akademiabaru.com/submit/index.php/arfmts/article/view/2924

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