A Chinese research group has investigated the effect of using phase change material (PCM) for cooling building-integrated photovoltaics (BIPV) panels and has developed a method for forecasting BIPV panel temperature using an artificial neural network (ANN).
PCMs can absorb, store, and release large amounts of latent heat over defined temperature ranges. They have often been used at the research level for PV module cooling and the storage of heat.
“The use of ANNs in predicting the performance of PV-PCM systems holds significant importance in advancing the efficiency and reliability of these integrated technologies,” the team said. “ANNs excel in capturing complex relationships within large datasets, allowing for a more accurate modeling of the intricate interplay between various factors affecting PV-PCM system performance.”
In the first part of their investigation, the academics developed a numerical model by simulating a BIPV system based on a polycrystalline module featuring cells with a thickness of 0.4 mm, a glass layer of 3 mm thickness, and an encapsulant of ethyl vinyl acetate (EVA) layer of 0.5 mm. The module included another layer of EVA, Tedlar with a thickness of 0.33 mm used on the rear surface, and an aluminum chamber of 0.5 mm with PCM was placed for cooling. This panel had a nominal efficiency of 17.5% at a reference temperature of 25 °C. Irradiation and ambient temperature were simulated as those of Jiangsu, China, on October 15, 2022.
“The highest and lowest difference between the electrical efficiency of the PV panel of systems with and without PCM is equal to 2.17% and 0.1%, respectively, which occur at 13:55 p.m. and 16 p.m.,” the scientists said. “The maximum and minimum difference between the efficiencies of the two systems is 2.17% and 0.10%, respectively, which occur at 13:55 p.m. and 16 p.m.”
Following these results, the academics developed the ANN forecasting method, using the deep learning algorithm group method of data handling (GMDH). “A multi-layer GMDH type neural network was applied to estimate the temperature of the solar cell of the PV-PCM system during the daytime using the input variables of total hourly solar irradiation and ambient temperature,” they explained.
For training, the researchers took 56 data points from their numerical model and then tested it on 24 forecasting points. “The correlation of coefficient (R2) is obtained as 0.97602,” they found. “In addition, the root mean square error (RMSE) and mean square error (MSE) were calculated as 1.483 and 2.22, respectively.”
The proposed approach reportedly achieved superior predictive performance compared to earlier methods.
However, the group also emphasized that “given that the model established in this study can only forecast the system's performance on a specific day, future research endeavors will focus on developing a model capable of predicting the system's performance throughout the entire year.”
The results were presented in “Forecasting the temperature of a building-integrated photovoltaic panel equipped with phase change material using artificial neural network,” published in Case Studies in Thermal Engineering. Scientists from China's Jiangxi University of Software Professional Technology, Aheadsoft Software's Postdoctoral Research Workstation, and Huaiyin Institute of Technology conducted the research.
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