An Empirical Approach to Solar Photovoltaic Cell Temperature Prediction
Publicado en línea: 06 oct 2024
Páginas: 422 - 436
Recibido: 15 mar 2024
Aceptado: 12 sept 2024
DOI: https://doi.org/10.2478/rtuect-2024-0033
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© 2024 Kudzanayi Chiteka et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting, especially in commercial power plants. The present study focused on the development of a hybrid machine learning based predictive model for solar photovoltaic cell temperature prediction in solar photovoltaic arrays. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for these parameters and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed to mimic experimentally measured data. Feature selection and dimensionality reduction were performed to reduce the input variables and maintain relevant data in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (