東華大學圖書館 |

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.
LDR:04993nmm a2200409K 4500 001 2355432
005 20230512095514.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798352684467
035 $a (MiAaPQ)AAI29352355
035 $a (MiAaPQ)USingapore216503
035 $a AAI29352355
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Nan, Zhang. $3 3695849
245 1 0 $a Development of a Behavior-Based Analysis Framework for Promoting RPV Deployment in Singapore.
264 0 $c 2021
300 $a 1 online resource (234 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
500 $a Advisor: Hwang, Bon-Gang ; Lu, Yujie.
502 $a Thesis (Ph.D.)--National University of Singapore (Singapore), 2021.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Innovations. $3 754112
650 4 $a Emissions. $3 3559499
650 4 $a Decision making. $3 517204
650 4 $a Neural networks. $3 677449
650 4 $a Biomass energy. $3 683849
650 4 $a Design. $3 518875
650 4 $a Alternative energy sources. $3 3561089
650 4 $a Households. $3 579926
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Alternative energy. $3 3436775
650 4 $a Behavioral psychology. $3 2122788
650 4 $a Energy. $3 876794
650 4 $a Psychology. $3 519075
650 4 $a Southeast Asian studies. $3 3344898
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0389
690 $a 0404
690 $a 0800
690 $a 0363
690 $a 0384
690 $a 0791
690 $a 0621
690 $a 0222
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a National University of Singapore (Singapore). $3 3352228
773 0 $t Dissertations Abstracts International $g 84-04B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352355 $z click for full text (PQDT)

	簡單查詢
	進階查詢
	圖書館推薦圖書
	讀者推薦圖書(公開)

	教師指定參考書
	借閱排行榜
	預約排行榜
	分類瀏覽
	展示書
	專題書單RSS

	個人資料
	個人檢索策略
	個人薦購
	借閱紀錄/續借/預約
	個人評論
	個人書籤
	東區互惠借書