Computational Modeling and Fuzzy Evaluation of Energy Efficiency in Public and Commercial Buildings Based on End-Use Energy Categories

Abstract

Energy efficiency assessment in public and commercial buildings plays a pivotal role in supporting sustainable development and energy conservation strategies. However, traditional evaluation approaches often fall short in accommodating the inherent uncertainty and subjectivity associated with building energy performance indicators. This study proposes a computational modeling framework based on fuzzy logic to assess energy efficiency using end-use energy consumption categories. Energy use data were categorized into six primary types: appliances & others, lighting, cooling, cooking & water heating, distributed heating, and central heating, which were normalized and processed through fuzzification, rule-based inference, and defuzzification stages. The proposed model was tested on a dataset from various building types in China, and validated through expert judgment. The results demonstrated that the fuzzy logic approach effectively captures nuanced energy consumption patterns and provides a more adaptable and interpretable evaluation metric compared to conventional binary classifications. The model offers insights into energy optimization potentials specific to different usage patterns. This research contributes a flexible and scalable methodology that can be implemented across various building typologies and geographies. The findings also highlight the potential for integrating fuzzy-based assessments into future smart energy management systems.