2013
DOI: 10.1007/s12652-013-0176-9
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Designing and testing decision support and energy management systems for smart homes

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Cited by 69 publications
(44 citation statements)
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References 29 publications
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“…Since some users might be selfish and only interested in optimizing their own costs, it is crucial to define proper billing tariffs for residential users to guarantee that each residential user is willing to cooperate with others and make a profit. According to [18], the billing function for each residential user can be defined as: (16) where:…”
Section: Cooperative Game Among Residential Usersmentioning
confidence: 99%
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“…Since some users might be selfish and only interested in optimizing their own costs, it is crucial to define proper billing tariffs for residential users to guarantee that each residential user is willing to cooperate with others and make a profit. According to [18], the billing function for each residential user can be defined as: (16) where:…”
Section: Cooperative Game Among Residential Usersmentioning
confidence: 99%
“…Consequently, prosumers' electricity networks in an SG will form a bidirectional market. DSM, together with the integration of distributed generation and energy storage [7][8][9][10][11][12][13] are considered increasingly essential elements of the SG [14][15][16].…”
Section: Introductionmentioning
confidence: 99%
“…Addressing this gap and contributing to theoretical synthesis in behavioral science, the present review paper links three areas of literature that have largely operated in parallel to suggest a new concept that can be applied to smart home contexts. In particular, in line with prior working showing that automation can improve energy savings [60], we discuss how green defaults can help to maximize the energy efficiency potential of certain smart home innovations. However, to maximize adoption rates and default acceptance, override options must be available, and the technologies themselves must be trustworthy.…”
Section: Conclusion and Recommendationsmentioning
confidence: 80%
“…A DR event is defined as a period of time during which the total power consumption of a household is to be limited within a demand limit specified by the utility. This is similar to 'scenario power' in [29,30], but in the Virginia Tech model, the demand limit is imposed only during the DR event to ensure peak load curtailment. Another variation of this implementation is possible where the utility specifies the amount of power (kW) to curtail instead of the demand limit.…”
Section: The Building Energy Management (Bem) Algorithmmentioning
confidence: 99%
“…Also, the customer selects the priorities and comfort level settings for the power-intensive loads. These settings can also be factory-preset considering load types, as in [29]. The BEM algorithm starts by collecting customer inputs of load priority and comfort level settings and utility inputs of demand limit and DR event duration.…”
Section: The Building Energy Management (Bem) Algorithmmentioning
confidence: 99%