Solar Energy Advances (Jan 2024)
Optimisation of the performance of a prototype instrument for measuring solar irradiation using artificial neural network
Abstract
Accurate measurement of solar radiation is essential for understanding climate patterns, assessing solar energy potential, and predicting weather conditions. Over the years, solar radiation measuring instruments such as pyranometers and solarimeters have been used to achieve this objective. However, the high cost of these standard instruments makes the technology less accessible, especially to students and academic researchers in low-to-middle-income countries. A low-cost prototype solarimeter has been developed that operates using a mini solar PV panel and a microcontroller. During testing, it was found that as temperature increased the instrument had significant accuracy deviations. As such, this study seeks to optimise the performance of the prototype solarimeter using Artificial Neural Networks (ANNs), a powerful data-driven machine learning tool for optimisation. Solar radiation data was simultaneously collected using the prototype solarimeter and a standard solarimeter. Corresponding ambient temperature was also recorded for each solar radiation measurement. The data was used to train the ANN model to learn data patterns and to predict accurate solar radiation in spite of the ambient temperature. Results of the study revealed that temperature has a negative correlation (–0.7381) with accuracy, such that an increase in temperature reduces the accuracy of the prototype solarimeter. Increased temperature caused an accuracy deviation of about 27.16 %. The ANN model successfully predicts accurate solar radiation measurement with an R-squared of 0.974, RMSE of 49.24 W/m2, and an accuracy of 86.32 % which represents a 13.39 % improvement in the performance of the prototype solarimeter. This study's novelty stands in its attempt to use machine learning to address the temperature sensitivity of a PV-based solar irradiance instrument. The results revealed here exposes the potency of deep learning models for optimising engineering systems.
