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dc.contributor.authorBasnet, Saurav Man Singh
dc.contributor.authorJewell, Ward T.
dc.identifier.citationS. M. Basnet and W. Jewell, "Predictive Analytics to Estimate Level of Residential Participation in Residential Demand Response Program," 2018 IEEE Conference on Technologies for Sustainability (SusTech), Long Beach, CA, USA, 2018, pp. 124-131en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractDemand response programs are becoming an integral part of the power system, helping create a closer alignment between the electrical service providers and customers. The research described in this paper uses the residential demand response (DR) program during a peak demand event. As in the marketing business, identifying target customers is vital in the DR program, thus making it more efficient and productive. Additionally, peak load events are very critical in the power system; therefore, it is essential to model an effective demand response program.The intent here is to use predictive analytics to estimate the level of residential participation in a DR program, and thus the load reduction capacity available, during peak load events. The research is divided into two different parts: apply predictive analytics to residents being considered for a DR program, and develop a residential DR model for each cluster obtained from predictive analytics.en_US
dc.relation.ispartofseries2018 IEEE Conference on Technologies for Sustainability (SusTech);
dc.subjectDemand responseen_US
dc.subjectDirect load controlen_US
dc.subjectHeating ventilation and air conditioningen_US
dc.subjectPredictive analytics and thermal integrityen_US
dc.titlePredictive analytics to estimate level of residential participation in residential demand response programen_US
dc.typeConference paperen_US
dc.rights.holder© 2018, IEEEen_US

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