To gain a fundamental understanding of the factors driving consumer energy behavior and for more effective policy-making, the development of energy consumption models taking into account key behavioral economic concepts is essential. In this direction, this paper presents a fuzzy logic decision-making model incorporating the concepts of bounded rationality, time discounting of gains, and pro-environmental behavior. The fuzzy model is used to characterize and predict consumer energy efficiency and curtailment behaviors in the context of residential cooling energy consumption. The model is developed from the perspective of the human decision-maker and the rules based on human reasoning and intuition. It takes into consideration monetary, personal comfort and environmental responsibility variables to yield predictions of one’s air-conditioning purchase and usage decisions. The results from running the model multiple times to simulate a real large urban population are found to match historical cooling energy use data reasonably well. This allows modelers some degree of confidence in the model. Moreover, perturbing key input variables produces plausible behaviors, thus providing additional validation to the model. This work demonstrates the feasibility of fuzzy logic as a powerful method for combining quantitative economic and physical factors with qualitative behavioral concepts in a single mathematical framework for better prediction of human energy behavior, and greater fundamental understanding of the “why” behind energy use that conventional building energy simulation models do not address.

为了对驱动消费者能源行为的因素有一个基本的了解，并且为了更有效地制定政策，必须考虑到关键的行为经济学概念来开发能源消耗模型。在这个方向上，本文提出了一种模糊逻辑决策模型，其中包含有限理性，收益的时间折现和亲环境行为的概念。模糊模型用于表征和预测住宅制冷能源消耗情况下的消费者能源效率和削减行为。该模型是从人类决策者以及基于人类推理和直觉的规则的角度开发的。它考虑到金钱，个人舒适度和环境责任变量，以预测空调的购买和使用决策。多次运行该模型以模拟一个实际的大城市人口的结果被发现与历史制冷能源使用数据相当吻合。这使建模人员对模型有一定程度的信心。此外，扰动关键输入变量会产生合理的行为，从而为模型提供额外的验证。这项工作证明了模糊逻辑作为将量化的经济和物理因素与定性行为概念结合在一个数学框架中以更好地预测人类能量行为的有力方法的可行性，