Energy conservation and energy efficiency measures in the residential sector are crucial for the successful implementation of long term climate change mitigation strategies. As residential buildings contribute significantly to the amounts of energy consumed and greenhouse gases (GHG) emitted within an economy, there is a great need for encouraging households towards a pro-environmental and energy-saving behavior. However, before any action for encouraging such behavior can be realized, it is of significant importance to increase the representation of human behavior concepts in energy modeling. As this representation is currently limited, policy-makers often fail to take into account behavioral economic insights but instead, they assume that consumers are motivated only by moneta...[ Read more ]

Energy conservation and energy efficiency measures in the residential sector are crucial for the successful implementation of long term climate change mitigation strategies. As residential buildings contribute significantly to the amounts of energy consumed and greenhouse gases (GHG) emitted within an economy, there is a great need for encouraging households towards a pro-environmental and energy-saving behavior. However, before any action for encouraging such behavior can be realized, it is of significant importance to increase the representation of human behavior concepts in energy modeling. As this representation is currently limited, policy-makers often fail to take into account behavioral economic insights but instead, they assume that consumers are motivated only by monetary incentives. This further hinders any effort for effective energy policy-making, giving rise to “energy efficiency gaps”.

In this direction, the primary objective of this thesis is to contribute to the development of energy models that consider key behavioral economic concepts, as a means to increase our understanding of the behaviors of energy users. To meet this objective, it 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 is based on rules that reflect human reasoning and intuition.

The main body of this thesis consists of three Parts. Part 1 provides the motivation for the overall work and highlights even further the need for encouraging behavioral change in energy management. It is doing so by assessing the effects of changing temperatures over the course of decades on the energy requirements for heating, ventilation and air-conditioning (HVAC) in residential buildings within large urban centers, by using a promising and simplified energy simulation tool, the equivalent full load hours (EFLH). As the results of this assessment demonstrate, such energy requirements can be expected to drastically increase over the next few decades, due to climate change. This trend may occur even with future stabilization and reduction of GHG. Such increase has significant implications, namely increased demands for additional primary energy, which will result in further GHG emissions. However, these effects can be controlled with adjustments to the regional or national electricity fuel mixes, and also by use of more efficient HVAC devices in residential buildings.

Part 2 of this thesis describes the development of the fuzzy logic-based framework towards a more holistic approach for residential energy decisions modeling. 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. Furthermore, to accommodate for the ambiguity in the environmental responsibility input (whose effects on residential energy behaviors are currently debatable) the model is configured to 4 alternative structures. The model is used to simulate the average monthly and annual cooling energy consumptions in Hong Kong, and the results from running it multiple times are found to match historical energy use data reasonably well. This allows modelers some degree of confidence in the fuzzy model, which is proven capable of making predictions of better quality compared to traditional estimation tools. Moreover, perturbing key input variables produces plausible behaviors, thus providing additional validation to the model.

Finally, Part 3 of this thesis analyzes a set of empirical data on energy behavior that are collected through a large scale survey conducted among Hong Kong households. This is done to address limitations derived from the subjectivity of fuzzy logic and the generally intangible nature of modeling human behavior. Apart from adding further realism and confidence in the model, the survey results are used to provide clearer understanding on the importance of pro-environmental psychological constructs among the population, and also on the potential of peer pressure and peer communication as tools to possibly encourage energy savings.

To sum up, 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. The trends revealed by the fuzzy model and the survey results are used to suggest further directions for the energy saving policies in Hong Kong. For instance, and consistent with expectations, the model shows that residential cooling energy consumption can be significantly reduced with increasing the environmental responsibility of the consumers or with increasing electricity prices. Thus, since electricity price increases are often met with strong social resistance, it is recommended energy strategies encouraging citizens to be more environmentally aware and responsible be implemented. As the survey results demonstrate, there is a great potential for peer pressure to positively influence pro-environmental behavior in the region and to significantly contribute to the targets of the HK3030 Campaign.