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Open Access

ARTICLE

Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions

Faisal Ghazi Beshaw1, Thamir Hassan Atyia2, Mohd Fadzli Mohd Salleh1, Mohamad Khairi Ishak3, Abdul Sattar Din1,*
1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Seberang Perai, 14300, Malaysia
2 Electrical Engineering Department, Tikrit University, Tikrit, 34001, Iraq
3 Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, P.O. Box 346, United Arab Emirates
* Corresponding Author: Abdul Sattar Din. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.061400

Received 23 November 2024; Accepted 03 March 2025; Published online 01 April 2025

Abstract

The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest Regressor. These models were deployed with the use of Shareable, Plot-interpretable Explainable Artificial Intelligence techniques, to improve trust in the AI. The findings suggest that our developed models are superior to the conventional models discussed in prior studies; with high Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values being close to perfection. In detail, the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 RMSE. Such outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’ decision-making procedures’ explanation. In addition to increasing prediction accuracy, this strategy gives stakeholders comprehensible insights, which facilitates improved decision-making and fosters confidence in AI-powered energy solutions. The outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.

Keywords

Renewable energy consumption; machine learning; explainable AI; random forest; support vector machine; decision trees; forecasting; energy modeling

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