Enhancement of Photovoltaic Performance With Coolant Calcium Chloride: Experimental and Predictive Modeling

This paper proposes a new method for predicting the energy generated by Photovoltaic (PV) panels with coolant Calcium Chloride (CaCl2). The study seeks to address heat-related issues that can affect the performance of PV panels by reducing their operational temperature. High operational temperatures can decreased electricity production, potentially cause permanent damage to solar cells, and shorten the panel’s lifespan. Calcium Chloride compound possesses hygroscopic properties, meaning it can absorb water from the surrounding air, thus serving as a cooling material. Calcium Chloride compound is applied as crystalline granules placed in a container beneath the PV panel. This research seeks to evaluate the effect of Calcium Chloride usage on reducing the operational temperature of PV panels and its influence on increasing PV efficiency. Subsequently, using the temperature data of the PV panel, modeling is performed to predict the PV output power using three predictive models: Multiple Linear Regression (MLR), Temperature Coefficient of Power (TC-P), and Artificial Neural Network (ANN). These three models are then compared to test their effectiveness. The research results indicate that using Calcium Chloride as a coolant is highly effective in improving the performance and efficiency of PV panels. Additionally, the ANN model accuratelly predicts power output based on temperature condition variations, with a Coefficient of Determination (
$R^{2} =0.98$
).