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Development of a Behavior-Based Analysis Framework for Promoting RPV Deployment in Singapore.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of a Behavior-Based Analysis Framework for Promoting RPV Deployment in Singapore./
作者:	Nan, Zhang.
面頁冊數:	1 online resource (234 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Innovations. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352355click for full text (PQDT)
ISBN:	9798352684467

Development of a Behavior-Based Analysis Framework for Promoting RPV Deployment in Singapore.
Nan, Zhang.

Development of a Behavior-Based Analysis Framework for Promoting RPV Deployment in Singapore. - 1 online resource (234 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Thesis (Ph.D.)--National University of Singapore (Singapore), 2021.

Includes bibliographical references

Residential Photovoltaic (RPV) systems are one type of building-integrated photovoltaics (BIPV) specialized for residential buildings. The deployment of RPV holds great potential to solve energy poverty and reduce greenhouse gas emissions because residential buildings consume approximately 38% of total electricity. However, it is challenging to make heterogeneous households adopt the system, causing a relatively slow development speed for RPV, especially in Southeast Asian countries. Singapore, an equatorial country with abundant solar resources, has installed 443.6 MWp total PV capacity but RPV only accounts for 3.5% by Q1 2021. To promote RPV deployment, researchers have conducted different analyses in many aspects, including exploring the driving factors for RPV adoptions, analyzing how RPV diffuses under different scenarios, and designing optimal energy policies for leveraging RPV adoption in the district.Household owners are the primary consumer of RPV systems, and they are heterogeneous in their adoption decisions and their energy usage behaviors. Behavioral factors are essential in driving RPV diffusion because RPV adoption is typical consumer behavior. However, there lacks a comprehensive study that links the RPV adoption, diffusion, and policy analysis in a unified framework in considering the complex consumers' behaviors. In conventional RPV adoption analysis, non-linear relationships are common in analyzing adoption behaviors, but they are often ignored in the existing studies due to the limitations of conventional data analysis approaches. On the other hand, Artificial Neural Networks (ANNs) are robust in dealing with non-linear relationships, but they lack interpretation capability. This leads to an Explainable Artificial Intelligence (XAI) issue and makes ANNs unsuitable for adoption analysis. Pertinent studies of diffusion analysis focused on using agent-based modeling (ABM) to simulate the consumers' technology adoption behaviors but did not capture consumers' countermeasures for investment volatility which impedes the RPV adoption and diffusion and undermines its economic feasibility. In addition, the existing research ignores the consumers' energy behavior after RPV installation, which may, in turn, influences the performance of energy policies.This research aims to analyze the mechanism of RPV adoption, diffusion and policy design considering the consumers' RPV adoption behaviors and afteradoption energy behaviors. An Artificial Intelligence (AI) assisted agent-based simulation framework is constructed to link behaviors among stakeholders in the process (household owners, policymakers). The proposed research framework consists of three critical objectives: individual behavior analysis, agent-based diffusion modeling, and energy policy optimization.(1) To resolve the XAI issue, this research firstly proposes a six-step analytical procedure based on a hybrid-ANN by integrating the behavior theory and the network weight-based method.(2) To capture investment volatility, this research integrated the real options analysis (ROA) approach into an agent-based diffusion model to quantify consumer's defer options in their decisions. The agent-based simulation model is able to simulate RPV diffusion in the network from individuals' behaviors and critical driving factors identified from objective one. (3) To explore the designing of optimal RPV policy considering consumers' adoption and energy usage behaviors by promising a simulation-optimization approach based on the agent-based model developed in objective two.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352684467Subjects--Topical Terms:

754112
Innovations.
Index Terms--Genre/Form:

542853
Electronic books.

Development of a Behavior-Based Analysis Framework for Promoting RPV Deployment in Singapore.
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