Explainable Artificial Intelligence Framework for Sustainable Energy Management and Data-Driven Decision Making

Abstract

In today's world with the considerable rise in the energy consumption of Energy Systems, the necessity to intelligent and sustainable management of energy has surfaced as a key requirement for energy efficiency and effective data-driven decision making. In recent years, AI and ML techniques have proven to be a promising way to predict energy consumption patterns, yet most of the existing solutions are black-box models, which lack interpretability. This limitation reduces trust in the users and lowers the likelihood of implementing AI solutions in the energy sector that promote sustainability. This study suggests an Explainable Artificial Intelligence Framework for Sustainable Energy Management and Data-Driven Decision Making to offer correct and clear energy analysis, combining machine learning based prediction with Explainable AI techniques. The proposed framework compares the performance of three models of machine learning namely Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) with an energy consumption dataset that features environmental and operational parameters. The performance of the models implemented are assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R²) of regression. Two methods of explaining the contribution of features and individual prediction results, namely, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) are embedded to explain the individual prediction outcomes and feature contribution in an enhanced transparency of the model. The results of the experiments confirm that the developed models have a good prediction performance, with the XGBoost model being the most successful approach for forecasting. Moreover, the explainability analysis reveals the most influential factors on energy consumption and gives valuable insights for optimization of sustainable energy. The proposed framework aims to balance predictive accuracy with interpretability of the models by converting traditional AI-based energy prediction models into transparent decision support models. The developed approach can help the energy manager and stakeholders to understand the consumption pattern, optimize the operational of the system and contribute in taking the initiatives for sustainable consumption.