article · Sustainability
Accurate solar radiation estimation is essential for renewable energy planning and grid integration, yet physical measurement equipment remains expensive and scarce. To address this limitation, machine learning offers an effective alternative by estimating radiation through multivariate time series analysis. A predictive framework combines recursive feature elimination with algorithms such as decision trees, Pearson correlation, logistic regression, gradient boosting, and random forest. Feature selection varies across models, with variables such as maximum and minimum temperature, precipitation, wind speed, and relative humidity proving influential depending on the chosen algorithm and time period. All tested models deliver reliable prediction metrics. Logistic regression achieves a root mean square error of 0.003, while alternative models record root mean square errors between 0.006 and 0.007 alongside coefficient of determination scores of 0.999.
Solar radiation drives weather patterns and provides renewable power, but its intermittency complicates energy generation and grid balancing. Developing computational models that accurately predict radiation using accessible meteorological factors helps overcome the cost and scarcity of specialised monitoring equipment. This supports more effective planning for renewable energy systems and improves environmental and climate studies.
This research presents an early-stage algorithmic framework that could enable software tools for renewable energy planning, solar power generation forecasting, and utility grid management. Prospective users include energy planners, grid operators, and climate analysts seeking radiation estimates without investing in expensive ground-based sensing hardware. Because the findings are based on model testing and feature evaluation rather than deployed operational software, the technology remains at an early computational stage.
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Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999.
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DOI: 10.3390/su151310609
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