Thesis

Modeling, analysis, and design of residential energy saving strategies: a systematic investigation of individual and social network influences

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Awarding institution
  • University of Strathclyde
Date of award
  • 2024
Thesis identifier
  • T16840
Person Identifier (Local)
  • 201469395
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Reducing energy consumption in the residential use sector could significantly improve energy savings, especially in this post-COVID era when working from home becomes a favored option for many people. Each home’s energy consumption pattern depends on individual user factors and is also influenced by the networks. Understanding the impacts of individual and social networks on household energy saving behaviours is an unexplored and challenging task. There’s a lack of systematic approach to enable modelling, quantitative analysis and optimization design. This motivates the research in this PhD project. The key innovative contributions are summarized in the following. Firstly, a small-scale network with detailed nodes connection information is studied. A network model is established, considering information diffusion within householders’ social network, from which the expected energy savings (EES) can be calculated. The connection level between users are weighted by coefficients. The influence from the source node to the target node is estimated by the use of probability theory. Considering information decay with different degree separations, the propagation coefficient that measures the diffusion of EES information can be calculated dynamically, based on which nodes that produce the largest EES are identified. From this study (in Chapter 4), the role of information diffusion within a social network in promoting energy efficiency products (EEP) is found to be significant. Secondly, factors that may affect user’s EEP adoption decision are analyzed. Four factors are selected: the personal acceptance level, the influence from the connected neighbors, the overall network adoption rate, and the advertisement influence from a wider environment. Among them, the personal acceptance is a grouped factor influenced by individual factors such as household income, age group, family status, and employment status. A network model is established to integrate multiple input factors towards their impacts on EEP adoption decision. Unknown model parameters are estimated using survey data. An adoption utility measure is defined based on the four impact factors with associated weightings, through which each user’s EEP adoption decision can be determined, and consequently the adoption rate of the whole network can be calculated. From this study (in Chapter 5), it is revealed that advertisement input plays a key role to influence user’s EEP adoption through a social network. Thirdly, the impact of advertisement on residential home EEP promotion is studied, with the aim to investigate the influence of advertisement control on energy savings through social networks. A mathematical model is established to predict the EES in a network where advertisement is used to influence the users’ EEP adoption decision. The proposed model consists of information diffusion, EES calculation, and advertisement control. It can be applied to mass roll-out programm to forecast the EES and the adoption rate of new energy products, based on which the advertising investment required to accelerate energy savings can be determined by optimization design. Epidemic theory is employed in modeling to characterize the dynamics of information diffusion on EEP. The change of response rate and adoption rate due to the influence from advertisement is quantified using Bayesian forecasting theory (BFT). Case studies for different scenarios are conducted, considering various optimization targets such as adoption rate, time cost, advertisement cost, and total energy savings subject to program budget and time constraints. This study (in Chapter 6) indicates the potential of using advertisement as a means to promote EEP. Two population networks are established using survey data, starting from a small network including 40 homes/nodes, followed by a large one with one million nodes. The small network is used for all three chapters (4-6), the large network is used for Chapters 5 and 6. The integrated work of network description, information diffusion modelling, parameter identification, sensitivity analysis and optimization design provides a useful tool to analyze and manage individual and social impacts on household energy savings.
Advisor / supervisor
  • Yue, Hong, 1970-
Resource Type
DOI

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