Machine Learning-Based Relative Performance Analysis of Monocrystalline and Polycrystalline Grid-Tied PV Systems

Abstract

In this research study, the design and performance evaluation of grid-tied photovoltaic systems has been carried out through experimentation, HelioScope simulation, and black-box machine learning methods for data-driven artificial intelligence system performance assessment and validation. The proposed systems are based on 15 kWp of monoperk and polyperk, which are separately installed in the industrial sector of Faisalabad, Pakistan. The experimental evaluation of the installed PV modules was performed from November 2020 to August 2021. The performance of the PV modules was evaluated by determining the annual average daily final yield (If), performance ratio (PR), and capacity factor (CF). The study showed that the annual average of daily final yield, performance ratio, and capacity factor for 15 kW polyperk was estimated to be 61.94 kWh, 84.17%, and 19.12, respectively. The annual average of daily final yield, performance ratio, and capacity factor for 15 kW monoperk was estimated to be 58.32 kWh, 81.42%, and 18.13, respectively. A comparison of final yield is obtained from simulation and real-time systems obtained from polyperk PV and monoperk. A significant mean error exists between the experimentation and simulation results which lie within the range of 1250 to 1470 kWh and 1600 to 1950 kWh, respectively. Substantial differences between both aforementioned results were initially tested and highlighted by statistical values; i.e., the standard error lies in-between 5 and 45% in polyperk crystalline and 5 and 25% in monocrystalline PV grid-connected module. Machine learning logistical regression evaluated that monoperk crystalline grid-connected system, experimental work was found to be more reliable with error difference reduces in off-peak months as compared to corresponding simulation study and vice versa for polyperk crystalline grid-connected system. Model accuracy after training and testing produced resulted up to 99.5% accuracy for either grid-connected experimentation or simulation outcomes with validation.

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