Chain-of-Thought Prompting for Generating Personalized Home Energy-Saving Strategies

This study aimed to demonstrate the effectiveness of a chain-of-thought (CoT) prompt tailored to generate personalized energy-saving strategies for households. Energy eco-systems grow increasingly complex on both demand and supply sides, but households often lack clear guidance on how to adjust their energy usage patterns in response to dynamic pricing structures and variable consumption patterns. While recent studies have indicated the potential of integrating large language models (LLMs) into home energy management (HEM) tasks, these efforts have primarily focused on technical and analytical applications, with limited attention to human-centered approaches that directly support occupants in managing their energy use. To address this gap, this study systematically assessed the performance of an LLM-integrated HEM system (HEMS) in generating personalized, data-driven, and behavior-oriented energy-saving recommendations for an actual household located in Austin, Texas. The proposed CoT prompt, incorporating advanced analytical and step-by-step reasoning processes - was compared against a concise, dialogue-type prompt to evaluate the effectiveness of structured reasoning in enhancing the accuracy and contextual relevance of LLM-generated recommendations. Results show that the CoT prompt achieved a mean accuracy of 71.3%, significantly outperforming the concise, dialogue-type prompt (29.3%). It indicated that structured reasoning enabled the LLM to generate appliance specific, data-driven, and context-aware interventions rather than generic, surface-level solutions. This study advances the understanding of how LLM-integrated HEMS can foster more intuitive, informed, and data-driven household energy decisions.