Forecasting daily solar radiation: An evaluation and comparison of machine learning algorithms

Abstract

Rising energy demand, resource depletion, and environmental issues tied to fossil fuels demand a transition to renewable energy. Solar power, abundant and well-established, presents a promising solution to address our expanding energy requirements. The sun radiates an astonishing amount of energy every second, far more than humanity’s current and future energy needs. Accurate solar radiation prediction is crucial for optimizing solar panel design, placement, and grid integration. This paper aims to predict daily global solar radiation data for six Pakistani cities: Karachi, Lahore, Islamabad, Quetta, Peshawar, and Multan. It highlights the importance of advanced algorithms and introduces an innovative data collection method using pyranometer sensors and microcontrollers, making data storage and analysis more affordable and efficient while reducing the financial burdens associated with traditional equipment. Focusing on Pakistan’s diverse solar radiation potential, this research evaluates eight machine learning algorithms using seven key statistical metrics to understand and compare their performance in predicting solar radiation. Four algorithms, k-nearest neighbors, Random Forest Regression, Gradient Boosting Regression, and Support Vector Regression (SVR), consistently exhibit remarkable precision, achieving outstanding R2 values of up to 99%. This highlights the crucial role of algorithm selection in solar radiation prediction, with SVR emerging as the top choice. SVR’s precise and reliable forecasts empower renewable energy planning and decision-making. This study provides valuable guidance for decision-makers to optimize solar energy utilization across diverse geographical regions and contributes invaluable insights to the field of renewable energy forecasting.