Willingness to participate in direct load control: The role of consumer distrust

Stenner, Karen; Frederiks, Elisha R.; Hobman, Elizabeth V.; Cook, Stephanie

doi:10.1016/j.apenergy.2016.10.099

Cited by 123 publications

(74 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , (y 1 , x 1 ) , x ∈ R n , y ∈ R (9) where each example x i is a space vector example; y i has a corresponding value; n is the size of training data. For classification: y i assumes finite values; in binary classifications: y i ∈ {−1, +1}; in digit recognition: y i ∈ {1, 2, 3, 4, 5, 6, 7, 8, 9, 0}; and for regression purposes, y i is a real number (y i ∈ R).…”

Section: B Reusementioning

confidence: 99%

“…With automatic participation in DR events, the house management system can reduce the electricity consumption based on the interaction with an external entity. This interaction is performed by smart meters, which enable bidirectional communications between the house and the grid [7], with measurements in small time intervals, the energy costs information in real time and the remote control of the electricity demand management [8], [9].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Pinto

Faia

Navarro-Cáceres

et al. 2019

IEEE Systems Journal

View full text Add to dashboard Cite

This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system. Index Terms-Building energy management, case-based reasoning (CBR), energy efficiency, multi-agent systems (MAS).

show abstract

Section: B Reusementioning

confidence: 99%

mentioning

confidence: 99%

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Pinto

Faia

Navarro-Cáceres

et al. 2019

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…Such information sharing raises questions about consumer privacy concerns, since some consumers may be unwilling to share appliance or household-level energy consumption data (Krishnamurti et al 2012;McKenna et al 2012;Gromet et al 2013;Naus et al 2014;Buchanan et al 2016). Participation in direct load control programs is also likely to be influenced by consumer trust in the technology and the utility or aggregator managing the program (Balta-Ozkan et al 2013;Stenner et al 2017). As with other smart grid technologies, the choice of appropriate frames in communications with consumers is likely to influence decisions to participate in such programs (Broman Toft et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Age and perceived benefits are associated with willingness to participate in an electric load control program

Howe¹

2018

Preprint

View full text Add to dashboard Cite

Load control programs allow power system operators to coordinate the power consumption of residential electric loads, such as air conditioners and refrigerators. Such programs can facilitate greater integration of variable renewable energy sources like wind and solar power into the grid. However, load control programs are uncommon in the U.S., and it is unclear what factors are associated with consumer willingness to participate in such programs. We examine the effect of demographic characteristics and types of benefits on willingness to share static and dynamic load information with the utility and participate in a load control program. A U.S. general population survey experiment presented respondents with short descriptions of refrigerator information sharing or control, with varying descriptions of benefits (including cost incentives and environmental benefits). Willingness to participate was highest when environmental benefits were described and among younger respondents, with respondent age having the largest effect. Respondents were more willing to share static than dynamic load information. Willingness to share dynamic information and participate in load control were comparable. These results suggest that targeted communication strategies that leverage consumer preferences for environmental benefits along with individual demographic characteristics may be most effective in eliciting adoption of load control programs.

show abstract

“…This may be particularly germane when it comes to adopting smart home technologies in the first place [3], and when consumers first begin interacting with innovations. For instance, Australian power customers with lower levels of trust in their utility were less likely to participate in utility-managed direct load control programs [50]. Muir [51] suggested that trust in automation develops in stages, beginning with predictability, moving onto dependability, and finally ending with faith.…”

Section: Trust In Automationmentioning

confidence: 99%

Adjustable Green Defaults Can Help Make Smart Homes More Sustainable

Sintov

Schultz

2017

Sustainability

View full text Add to dashboard Cite

Smart home technologies offer exciting opportunities to promote more efficient uses of energy. For instance, programmable thermostats, centralized lighting controls, and rooftop solar panels all have potential for energy conservation and efficiency. However, these technologies alone will not guarantee energy savings. Whereas previous research on smart homes has focused on the technologies themselves, relatively little work has addressed the factors that shape the human-technology interface. In this review paper, we argue that in order to ensure any savings, smart home technologies must first be adopted by end-users, and once adopted, they must be used in ways that promote energy efficiency. We focus on three areas of behavioral research with implications for smart home technologies: (1) defaults; (2) perceived adjustability or control; and (3) trust in automation. Linking these areas, we propose a new concept for improving the efficiency gains of smart homes. First, although smart device controls can help save energy, considerably larger energy efficiency gains can be realized through smart automation. But importantly, the default settings of systems should be "green", to maximize energy savings. Second, many people have concerns around relinquishing decision-making to technologies, which can reduce the likelihood of adoption. People want to be, or at least to feel, in control of their homes, even if they do not adjust settings post-installation. Further, consumer trust in technologies encourages adoption in the first place; trust also impacts consumer interactions with installed devices and can impact default acceptance. Combining these concepts, we recommend that smart home technologies build consumer trust and come pre-programmed with adjustable green defaults, which permit consumers to change initial green settings.

show abstract

Willingness to participate in direct load control: The role of consumer distrust

Cited by 123 publications

References 94 publications

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Age and perceived benefits are associated with willingness to participate in an electric load control program

Adjustable Green Defaults Can Help Make Smart Homes More Sustainable

Contact Info

Product

Resources

About