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dc.contributor.authorferreira, raul
dc.contributor.authorDal Pont, Maurício
dc.contributor.authorTeixeira, Wendell
dc.date.accessioned2019-07-24T12:37:45Z
dc.date.available2019-07-24T12:37:45Z
dc.date.issuedJune 2019
dc.identifier.isbn978-2-9602415-0-1
dc.identifier.issn2032-9644
dc.identifier.urihttps://cired-repository.org/handle/20.500.12455/171
dc.description.abstractA distribution utility has to deal with several customer calls regarding grid maintenance or energy issues. Generally, when the proper channels receive a call from the customers, the reported issue pass through a screening phase and, in the end, a maintenance team is sent to the location to solve the problem. However, not all problems are responsibility of the company, generating an unnecessary displacement for the maintenance team, a problem denominated as “improper dispatch”. Improper dispatches generate high costs regarding fuel and logistic. Besides, a high number of improper dispatches can result in heavy penalties to the company since the staff is not available to attend customers that really would need assistance. For tackling this problem, we propose a supervised machine learning solution that uses the customer calls information to classify when a call is improper or not. Our first results indicate that our model achieves up to 80% of assertiveness within a real dataset from the industry. In this work, we show how we built this model, pre-processed the information and, how this solution can be applied to decrease maintenance costs inside an energy company.
dc.language.isoen
dc.publisherAIM
dc.relation.ispartofseriesCIRED Conference Proceedings
dc.titleReal-time decision support system applied to distribution utility dispatches
dc.typeConference Proceedings
dc.description.conferencelocationMadrid, Spain
dc.relation.ispartProc. of the 25th International Conference on Electricity Distribution (CIRED 2019)
dc.contributor.detailedauthorferreira, raul, Universidade Federal do Rio de Janeiro, Brazil
dc.contributor.detailedauthorDal Pont, Maurício, universidade federal de santa catarina (ufsc), Brazil
dc.contributor.detailedauthorTeixeira, Wendell, CPFL, Brazil
dc.date.conferencedate3-6 June 2019
dc.description.peerreviewedYes
dc.title.number68
dc.description.openaccessYes
dc.contributor.countryBrazil
dc.contributor.countryBrazil
dc.contributor.countryBrazil
dc.description.conferencenameCIRED 2019
dc.contributor.affiliationUniversidade Federal do Rio de Janeiro
dc.contributor.affiliationuniversidade federal de santa catarina (ufsc)
dc.contributor.affiliationCPFL
dc.description.sessionOperation, control and protection
dc.description.sessionidSession 3


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