Large Language Model-Driven Context-Aware Eco-Feedback Generation and Evaluation

Abstract

The objective of this study was to demonstrate the potential of generating eco-feedback that accounted for unique household contextual information, named as context-aware eco-feedback, through a large language model-integrated framework. Previous studies have introduced personalized eco-feedback, mostly relying on household energy use patterns; however, they frequently did not reflect distinct household characteristics, including their persona or non-negotiable routines, leaving eco-feedback ineffective and sometimes superficial. To address these limitations, we introduced a contextual engineering framework that generated eco-feedback using a self-consistency with chain-of-thought prompt that leveraged household energy analysis data, utility rate structures, and characteristic information. We conducted a rigorous empirical validation and a combinatorial evaluation analysis to assess this framework systematically. The former aimed to test the framework's ability to generate accurate and data-driven eco-feedback, customized to given contexts by comparing it with reference interventions. The latter aimed to reveal the framework's adaptability across diverse household contexts by investigating how context-aware eco-feedback changed. Key findings were the following: our proposed framework generated eco-feedback that aligned with reference solutions at a mean accuracy of 92.0% across different household configurations, accurately leveraging the provided household data for feedback generation (95.7% of data citation accuracy). Also, it was largely adaptive to diverse household contexts, significantly shifting targeted appliances and energy-saving strategies. Ultimately, this study contributes to realizing the next level of context-aware interactions between occupants and buildings which paves the way for higher occupant living quality and sustainability.