Household energy resilience: Shifting perspectives to reveal opportunities for renewable energy futures in affluent contexts

Hasselqvist, Hanna; Renström, Sara; Strömberg, Helena; Håkansson, Maria

doi:10.1016/j.erss.2022.102498

Cited by 45 publications

(27 citation statements)

References 66 publications

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“…Other studies highlight that desires other than utility and environmental concerns are why such technologies are brought into the home [20,30,46,59,60,64]. Together our findings suggest both alignments and misalignments between what designers of these technologies assume about the engaged householder and what householders are expecting from these technologies, and how they should co-perform in everyday practices [25,37]. To better encounter household's expectations in the development of future energy systems, we see exciting opportunities for HCI researchers and designers to create interaction designs that move away from evaluating how effective, effortless, and convenient these environmental benefits may come about, and instead focus on the fun, playful and meaningful of experiencing future energy systems [7,28,34,60].…”

Section: Design Visions Of Future Energy Technologymentioning

confidence: 53%

Design Visions for Future Energy Systems: Towards Aligning Developers’ Assumptions and Householders’ Expectations

Raptis

Šikšnys

Pedersen

et al. 2022

Nordic Human-Computer Interaction Conference

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We increasingly see smart energy technology moving into peoples' homes, designed to support householders in strategically managing different forms of energy conservation. Often, design visions for such technologies assume householders will accept smart energy systems as long as the interaction remains effortless, efficient, and convenient. Likewise, developers rely on assumptions that smart energy technologies meeting such usability goals may urge householders to become active players in a future energy market. Yet, a growing body of HCI research illustrates that actively engaging people with sustainability issues through technology requires more than merely meeting usability goals. Against this backdrop, our study investigates the design vision of a cutting-edge home energy management system created as part of an EU project and householders' expectations of it. Our findings highlight (mis)alignments that emerged from a questionnaire study with 167 households participating in a field trial of the developed technology. We use the insights to discuss the alignment of sustainable visions, HCI, and the design of future energy systems.CCS Concepts: • Human-centered computing → Empirical studies in HCI.

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Section: Design Visions Of Future Energy Technologymentioning

confidence: 53%

Design Visions for Future Energy Systems: Towards Aligning Developers’ Assumptions and Householders’ Expectations

Raptis

Šikšnys

Pedersen

et al. 2022

Nordic Human-Computer Interaction Conference

View full text Add to dashboard Cite

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“…(1) Energy security. The energy security dimension assesses energy supply security, which can be seen as the ability to provide energy consumption security; in other words, the ability to cater for residents’ energy needs, and ensure the security and resilience of the energy system (Ahmadi et al , 2021; Hasselqvist et al , 2022; Zhang et al , 2022). Among these three specific indicators, energy supply security is measured by natural gas supply and liquefied petroleum gas supply, which are two commonly used energies.…”

Section: Quantifying the Energy Trilemma In Chinamentioning

confidence: 99%

Quantifying the energy trilemma in China and assessing its nexus with smart transportation

Zhao

Dong

2022

SRT

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Purpose Mitigating the energy trilemma (ET) is of great importance for dealing with climate change and realizing carbon neutrality. To this end, effectively assessing the level of the ET is essential. The purpose of this study is to evaluate the current situation and the spatio-temporal changes of the ET in the whole of China. Design/methodology/approach Moreover, based on provincial-level data in China for the period 2002–2017, and by using the dynamic estimation model, we aim to determine the specific marginal impacts of smart transportation (ST) on the ET, and the possible channels through which ST works on the ET. Findings We thus present the following findings: (1) The performance of both ET and its three pillars is gradually improving in China. Moreover, the situation tends to vary dramatically among various regions and provinces, and the gap between the best performers and the worst is large. (2) ST plays a significant role in inhibiting the ET, a finding that remains robust after a series of tests. And (3) the ET eradication effect of ST is caused mainly by improved innovation, advanced technical efficiency, and the increasing energy scale. Originality/value Accordingly, we put forward some policy recommendations to help tackle ET and accelerate ST in China.

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“…However, more recent studies by Sharifi and Yamagata (2016b), Ohshita and Johnson (2017), Ragheb et al (2017), andNik et al (2021) indicate how energy systems can be integrated into the infrastructure that fosters urban resilience. Moreover, Hasselqvist et al (2022) provide a complex perspective of such resilience, by using households as a starting point. Conversely, several works focus on AI applications and their contributions to building urban systems, resilience in general, infrastructure resilience, and a sustainable urban environment (Abdul-Rahman et al, 2021;Bibri, 2021a;Haggag et al, 2021;Huang and Ling, 2019;Huang and Wang, 2020;Konila Sriram et al, 2019;Ladi et al, 2022;Ortiz et al, 2021;Rahimian et al, 2020;Tekouabou et al, 2021;Zhang et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Urban energy system initiatives and their ties to UER pathways and subpathways. Source: The authors' insights, based on reviewing (Hasselqvist et al, 2022;Sharifi, 2016Sharifi, , 2019Sharifi and Yamagata, 2014a…”

Section: Ai Technology and Applicationsmentioning

confidence: 99%

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Hassan

Megahed²

2022

A&J

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Introduction:Climate change and global warming are among the greatest challenges facing the world today. A newconcept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale datamining, advanced algorithms, and real-timemonitoring. However, veryfewstudies have investigated howsuch aspectscan be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysisand a systematic review of UER in accordance with AI concepts, models, and applications. Results:The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty:The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialistsand decision-makers aiming to expand AI and ML applications to optimize UER.

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Household energy resilience: Shifting perspectives to reveal opportunities for renewable energy futures in affluent contexts

Cited by 45 publications

References 66 publications

Design Visions for Future Energy Systems: Towards Aligning Developers’ Assumptions and Householders’ Expectations

Design Visions for Future Energy Systems: Towards Aligning Developers’ Assumptions and Householders’ Expectations

Quantifying the energy trilemma in China and assessing its nexus with smart transportation

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

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