Predicting the performance of a photovoltaic unit via machine learning methods in the existence of finned thermal storage unit

Abstract

The process of cooling the photovoltaic (PV) cells is essential since increasing solar cell temperatures reduces electrical effectiveness. One suggested method to reduce cell temperature is employing PCM (phase change material) mixed with nanomaterial. Thermoelectric modules between the layers are also recommended to increase electrical efficiency. By taking advantage of machine learning predictive algorithms, the objective is to forecast the effectiveness of a PV unit equipped with finned thermal storage unit in existence of nanomaterials, while maintaining an appropriate accuracy based on two evaluation metrics throughout prediction. According to the data analytics of the simulation, the selected models used to achieve this goal are linear regression, polynomial regression, lasso regression, and an Auto-Regressive Integrated Moving Average (ARIMA) model. These models were then examined employing the root mean squared error (RMSE) and mean absolute percentage error (MAPE) across three cases, namely plain mode, mode with fins, and plain mode with the effect of gravity. The test RMSE and MAPE of these models across the above-mentioned cases were then calculated for comparison purposes, where the best-performing model for predicting both PCM temperature and melting fraction (MF) was ARIMA, with an average RMSE of 0.11, and an average MAPE of 0.0003 for predicting PCM temperature and an average RMSE of 0.003, and an average MAPE of 0.0034 for predicting MF across all cases. The ARIMA model was then used to predict the MF, PCM temperature, and PV temperature of the last case, namely mode with fins and the effect of gravity. Finally, this model was also used to predict the time at which the PCMs are completely melted in all modes, as well as to predict PV temperature. The total electrical efficiency of the mode with fins and the effect of gravity were calculated using the predicted PV temperature, yielding a result of 13.993 %.