Data-Driven Insights for Smart Energy Efficiency Management of a Heat Pump Water Heating System

Efficient management of industrial water heating systems is critical in the era of smart cities. This study tackles the inefficiencies of fixed temperature presets in traditional systems, leading to underheating or overheating due to dynamic usage patterns. The historical data obtained from the U.S. Department of Energy (DOE) was used to develop a clustering model through comprehensive segmentation analysis. The K-nearest neighbor (KNN) algorithm, achieving a silhouette score of 0.7, effectively segmented demand into high, medium, and low levels. This segmentation guided the application of a Genetic Algorithm (GA) to optimize temperature set points and deadband temperatures. The optimized model demonstrated potential monthly energy savings of $20-35 {\%}$. This methodology underscored the importance of adaptive, data-driven approaches to enhancing energy efficiency. By integrating segmentation analysis with GA-based optimization, this research established a foundation for smarter and more responsive control frameworks in heat pump water-heating systems. The findings highlighted the significant impact of data-driven management on energy conservation, offering a strategic pathway for future advancements. Future work will focus on developing predictive models for dynamic load profiles to enhance the proposed control strategies. This study is a pivotal step toward achieving greater energy efficiency and sustainability in industrial water heating systems.