Smart AI Framework for Sustainable Data Center Heat Recovery

Abstract

The rapid growth of cloud computing, artificial intelligence (AI), big data analytics, and Internet of Things (IoT) applications has significantly increased the energy consumption of modern data centers. A substantial portion of this energy is converted into waste heat, requiring extensive cooling systems that consume additional power and reduce overall energy efficiency. Although several waste heat recovery technologies have been developed, most existing approaches primarily focus on recovering thermal energy and provide limited support for intelligent heat utilization and decision-making. This paper proposes an AI-Based Sustainable Data Center Heat Recovery Framework that integrates IoT sensors, cloud computing, data preprocessing, Random Forest Regression, and AI-based decision-making into a unified intelligent energy management system. The proposed framework continuously monitors operational parameters, predicts future heat generation, and recommends the most suitable heat recovery application based on operational conditions and demand. By combining predictive analytics with intelligent heat distribution, the framework improves energy efficiency, reduces cooling costs, minimizes carbon emissions, and enhances the sustainable utilization of recovered thermal energy. The proposed system provides a scalable and intelligent approach for developing energy-efficient and environmentally sustainable data center infrastructures. Keywords: Predictive Analytics; Artificial Intelligence (AI); energy efficiency; Data Center; Waste Heat Recovery; Sustainable Energy