Targeted Messaging for Appliance-based Demand Response

Chatzigeorgiou, Ioanna; Diou, Christos; Ανδρέου, Γεώργιος

doi:10.1109/upec49904.2020.9209823

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…Bottom-up end-use modeling has been widely used during recent years as a potential part of effective DR prediction and semi or full ADR rescheduling. Variations of Markov models [18,19], neural networks [20], classifiers [21], and event-based approaches have been used in the literature [22][23][24][25] to acquire appliance models that can successfully estimate their energy use or potential flexibility.…”

Section: Related Workmentioning

confidence: 99%

“…For these reasons, in [25] a targeted messaging mechanism was presented that aims to improve DR communication services by suggesting appliance-based DR actions to the end users during a peak DR event, based on their past-usage appliance information. To this purpose, it is assumed that data regarding the past energy use are available through a SM and appliance data are available either via Non-Intrusive Load Monitoring (NILM) algorithms or via smart plugs.…”

Section: Contributionmentioning

confidence: 99%

“…This study builds upon an earlier work on a probabilistic appliance modeling scheme for DR alerts [25] by examining variations of the initial model and reassessing the approach for more single-appliance and multi-appliance scenarios.…”

Section: Appliance Modelingmentioning

confidence: 99%

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Demand Response Alert Service Based on Appliance Modeling

et al. 2021

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Demand response has been widely developed during recent years to increase efficiency and decrease the cost in the electric power sector by shifting energy use, smoothening the load curve, and thus ensuring benefits for all participating parties. This paper introduces a Demand Response Alert Service (DRAS) that can optimize the interaction between the energy industry parties and end users by sending the minimum number of relatable alerts to satisfy the transformation of the load curve. The service creates appliance models for certain deferrable appliances based on past-usage measurements and prioritizes households according to the probability of the use of their appliances. Several variations of the appliance model are examined with respect to the probabilistic association of appliance usage on different days. The service is evaluated for a peak-shaving scenario when either one or more appliances per household are involved. The results demonstrate a significant improvement compared to a random selection of end users, thus promising increased participation and engagement. Indicatively, in terms of the Area Under the Curve (AUC) index, the proposed method achieves, in all the studied scenarios, an improvement ranging between 41.33% and 64.64% compared to the baseline scenario. In terms of the F1 score index, the respective improvement reaches up to 221.05%.

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