Output Power Forecasting for 2kW Monocrystalline PV System using Response Surface Methodology
Abstract
Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic is used. The 5 minute sampling size of yearly 2014 weather station data the three environmental elements and output power of a 2kW Monocrystalline real PV system are used for training. Whereas, yearly 2015 data of the aforementioned elements are used for validation. The coefficient of determination (R2) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results show that, full quadratic is the most accurate prediction model with R2 value of 0.9995 and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer.
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