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Ke, G., et al. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4-9 December 2017, 3149-3157.

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• TITLE: An Empirical Analysis on Renewable Energy: Biogas Production Prediction Using Machine Learning

  AUTHORS: Md. Mahedi Hassan, Arif Hossen, Md. Nurunnabi Sarker, Yeasin Arafat, Aslam Khan, Shafiqul Islam Talukder, Bikash Kumar Saha Roy

  KEYWORDS: Biogas, Renewable Energy, Energy Production Management, Machine Learning, XAI, Analysis, Shapash

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.13 No.7, July 29, 2025

  ABSTRACT: Biogas is gaining prominence as a renewable energy source with significant potential to reduce greenhouse gas emissions and mitigate environmental impacts associated with fossil fuels. This study presents an improved biogas production estimation method using machine learning (Ridge Regression, Lasso Regression, Random Forest, XGBoost, LightGBM, and GBM) combined with explainable AI (XAI) techniques to enhance model interpretability. Our rigorous evaluation using Wilcoxon Signed-Rank Tests demonstrated that LightGBM and XGBoost consistently outperformed other algorithms—LightGBM achieved superior performance in the 70:30 train-test split (RMSE = 0.075, R2 = 0.895), while XGBoost excelled in both 80:20 (RMSE = 0.091, R2 = 0.847) and 50:50 splits. These models proved significantly better than traditional methods (p