Towards sustainable energy management: Leveraging explainable Artificial Intelligence for transparent and efficient decision-making

Abstract

Sustainable energy management techniques are now more important than ever as the world’s energy demand rises. Artificial Intelligence (AI) has showed a great deal of promise in this domain. But trust and accountability are challenged by AI decision-making’s opaqueness, especially in crucial areas like sustainable energy management. This research offers an innovative algorithm called Eco-friendly Explainable Artificial Intelligence (EcoXAI) to address the challenges involved in sustainable energy management. The proposed EcoXAI comprises five components: data collection and preprocessing, feature engineering and selection, Explainable Artificial Intelligence (XAI), decision-making, and stakeholder engagement and reporting. EcoXAI uses XAI to promote transparent and efficient decision-making in the field of renewable energy sources. Especially when it comes to projecting solar and wind energy, the suggested EcoXAI algorithm provides a ground-breaking answer to the major problems in sustainable energy management. Through the use of XAI, EcoXAI bridges the gap between complex Machine Learning (ML) models and human comprehension by providing stakeholders with transparent and accurate decision-making tools. The research results highlight EcoXAI’s amazing potential. When comparing several ML techniques for solar power forecasting, it can be seen that the XGBoost and Linear Regression models perform exceptionally well, with respective accuracy rates of 99.394% and 99.387%. EcoXAI’s exceptional accuracy enables it to produce extremely dependable projections, empowering stakeholders to manage resources wisely and make well-informed decisions.