energy forecasting

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• Published Subscription Original Research 19 Sep 2025

  AI/ML-Based Approach to Solar Irradiance Prediction and Energy Suitability

  By Kavya Srivastava, Krati Garg, Palak Agarwal, Pallavi Gupta, Ravi Kumar

  Abstract: In this paper, due to challenges in precisely predicting solar irradiance, which is essential for solar power system optimization, we employed six diverse machine learning (ML) techniques: Linear Regression, Decision Tree, Random Forest, Gradient Boosting methods (including XGBoost), and Neural Networks—to analyze and predict outcomes using a dataset containing meteorological and temporal features. Key variables include wind speed, humidity, and temperature, which significantly influence the model’s predictive capability. Each method …

  Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 3, 2025 · pp. 36–48 Read article →