Fuzzy Model Of Residential Energy Decision-Making: Considering Behavioral Economic Concepts

As residential buildings can account for up to 50% of the total energy consumption and greenhouse gas (GHG) emissions in an economy, there is a need for the development and implementation of policies to encourage households towards pro-environmental energy-saving behavior.

However, such policies in practice often fail to take into account behavioral economic insights but instead, assume consumers are motivated only by monetary incentives. This hinders their effectiveness, giving rise to “energy efficiency gaps.” Thus, for better energy policies, it is necessary to increase consideration of behavioral economic concepts in energy modeling. However, efforts to do so have thus far been restricted by the lack of a comprehensive systematic way to integrate these concepts with traditional energy modeling.

Researchers from The Hong Kong University of Science and Technology have developed a fuzzy logic-based model to combine insights from social sciences and behavioral economics (on bounded rationality, time discounting of gains, and pro-social behavior) with more quantitative aspects of energy consumption in a single mathematical framework. The fuzzy model is capable of characterizing and simulating consumer energy efficiency and curtailment behaviors in the context of residential cooling energy consumption. A unique feature of it is its perspective that is from the human decision-maker as can be seen from the rules governing it which have been developed to reflect human reasoning and intuition.

The fuzzy model comprises three fuzzy inference systems (FIS), each one of which receives a number of inputs and delivers a single output. This is realized through an inference mechanism and a set of fuzzy if-then rules. The inputs include monetary, personal comfort and environmental responsibility parameters that are deemed influential for one’s air-conditioning (AC) purchase and usage decisions, and the outputs represent these decisions.

Through the appropriate membership functions (a key feature in fuzzy logic), all these inputs and outputs are connected with linguistic terms that represent human perceptions (e.g. “cold” or “hot” temperature, “cheap or “expensive” price of electricity, etc.). Furthermore, the model accounts for the ambiguity in the relationship between certain decision-making drivers and actual energy behaviors. More specifically, it reflects the debate in the relative literature on whether pro-environmental psychological constructs constitute antecedents of just energy efficiency behavior or curtailment behavior (or both), by considering approximately 30 alternative scenarios; these differ in their treatment of the environmental responsibility parameter and in their hypothetical distribution of its values among a population.

The simulation of the average monthly and annual cooling energy consumptions in Hong Kong is used as the case study, and the results are found to match historical energy use data reasonably well. Moreover, perturbing the model’s key input variables produces plausible behaviors, thus providing additional validation to it. Finally, comparing the fuzzy model results with the energy consumption estimates based on traditional methods (such as the estimation of cooling energy consumption based on EFLH — Equivalent Full Load Hours) suggests the fuzzy model is capable of producing predictions of comparable or even better quality than such methods.

Overall, this work has demonstrated the feasibility of fuzzy logic as a powerful method for combining qualitative behavioral concepts with quantitative economic and physical factors in a single mathematical framework. This can open the way 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. Subsequently, this can lead to more effective planning and management, not only of energy use, but also of any other common process or activity where human behavior plays a key role.

These findings are described in the article Fuzzy Model of Residential Energy Decision-Making Behavioral Economic Concepts, recently published in the journal Applied Energy. They were also presented in the recent 2017 Behavior Energy and Climate Change (BECC) conference in Sacramento, CA. This work was part of the doctoral thesis of Dr. Constantine Spandagos, supervised by Dr. Tze Ling Ng from the Hong Kong University of Science and Technology.