Leveraging IoT, Big Data and AI Capabilities for Technological Regenerative Innovation in Commercial and Industrial Energy Management

Traditional energy management systems in the commercial and industrial (C&I) built environment rely on reactive maintenance and fixed operational parameters, leading to suboptimal energy efficiency and increased operational costs. This paper presents a comprehensive data driven framework that integrates Internet of Things (IoT) sensors, Big Data analytics platforms, and Explainable Artificial Intelligence (XAI) to transform energy management from reactive to predictive framework. The proposed technological regenerative innovation(TRI) framework employs real time IoT enabled condition monitoring to collect granular data related to mostly energy consumption, which is processed through machine learning algorithms or SHAP (SHapley Additive Explanations) analysis providing interpretable insights into energy consumption patterns and decision making strategies as compared to conventional statistical analysis, to ensure transparency and stakeholder trust. Two case studies show the practicality of the TRI framework's effectiveness: (i) a commercial chiller system and (ii) an industrial chiller facility in which hourly data was considered which showed predictive accuracy with ML models. The industrial case study presented critical operational parameters through SHAP analysis, identifying key parameters driving primary energy consumption. A scalable TRI framework, applicable for autonomous energy systems, increases improvement in predictive and provides actionable XAI insights enabling targeted energy analysis for system efficiency and sustainability. Through practical implementation, proposed TRI framework provides energy managers with transparent, AI-driven tools that shift energy management from reactive maintenance to predictive optimization, delivering actionable insights through SHAP analysis and reliable forecasting capabilities.