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dc.contributor.advisorFarmanbar, Mina
dc.contributor.advisorChakravorty, Antorweep
dc.contributor.advisorMehdipourpirbazari, Aida
dc.contributor.authorResulaj, Redjol
dc.contributor.editorResulaj, Redjol
dc.coverage.spatialStavanger, Norwaynb_NO
dc.date.accessioned2019-10-07T07:52:44Z
dc.date.available2019-10-07T07:52:44Z
dc.date.issued2019-06-12
dc.identifier.urihttp://hdl.handle.net/11250/2620507
dc.descriptionMaster's thesis in Computer Sciencenb_NO
dc.description.abstractThe focus of this thesis is the use of machine learning algorithms to perform next step short term load forecasting on fifty five households in Stavanger, Norway. A dataset containing electricity consumption data for more than one year is used to train and evaluate a Feedforward Neural Network model and a Random Forest model. Weather data, atmospheric data and calendric variables are also used to aid the forecasting task. First, the implementation of the two models is introduced. Their architectures are given and the rationale behind the design principles are explained. Then, for every household, a separate neural network and random forest model are trained using the training dataset. The models are tested using the testing dataset, to evaluate the models’ accuracy. The models were trained and tested on three different but equivalent datasets. The difference between them was the time resolution of th edata. These resolutions are 1 hour, 15 minutes and 1 day. The implemented models achieved various levels of accuracy depending on the household and the data resolution. Generally, the implemented Neural Network achieved higher accuracy than its Random Forest counterpart. It was also discovered that the resolution has a big influence on the outcome of the next step short term load forecasting task.nb_NO
dc.language.isoengnb_NO
dc.publisherUniversity of Stavanger, Norwaynb_NO
dc.relation.ispartofseriesMasteroppgave/UIS-TN-IDE/2019;
dc.subjectShort term load forecastingnb_NO
dc.subjectsmart metersnb_NO
dc.subjectinformasjonsteknologinb_NO
dc.subjectdatateknikknb_NO
dc.subjectdatateknologinb_NO
dc.subjectmaskinlæringnb_NO
dc.subjectmachine learningnb_NO
dc.subjectnevrale nettverknb_NO
dc.subjectrandom forestnb_NO
dc.subjectartificial neural networknb_NO
dc.titleSmart Meter Based Load Forecasting for Residential Customers Using Machine Learning Algorithmsnb_NO
dc.typeMaster thesisnb_NO
dc.description.versionsubmittedVersionnb_NO
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550nb_NO


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  • Master's theses (TN-IDE) [278]
    Masteroppgaver i Teknologi/sivilingeniør: informasjonsteknologi, datateknikk / Masteroppgaver i Teknologi/sivilingeniør: kybernetikk, signalbehandling

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