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dc.contributorDepartment of Building Environment and Energy Engineeringen_US
dc.contributor.advisorWang, Shengwei (BEEE)en_US
dc.contributor.advisorXiao, Fu (BEEE)en_US
dc.creatorXu, Kan-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/12115-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleLoad prediction for peak demand limitingen_US
dcterms.abstractIn commercial and non-residential building, the electricity bill always contains the power consumption and the peak demand in the bill period, and the latter one may contribute to more than half of the bill with a relatively short occurrence time. To reduce the peak demand, many demand limiting strategies have been developed, and load predication is an important part which can guide the use of the limiting method. This project aims to analyze the performance of different machine learning models and effects of different prediction horizons on building load prediction.en_US
dcterms.abstractIn this project, Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) are learned, and Multi-Layer Perceptron (MLP), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit (GRU) are used to realize the load prediction of a commercial building. The training and prediction of the model only concern the on-peak time, the 7-days ahead prediction result are used to compare the accuracy of the prediction model with three different algorithms. The result shows that the RNN is better than the ANN in the prediction accuracy, and GRU is better than LSTM. As for the different input and output horizons, when the input horizon is the same, the one-day output horizon has the highest prediction accuracy. When the output horizon is the same, the seven-days input horizon has the highest prediction accuracy. Based on the different time of the input, two different structures of the prediction model are proposed. In the common one, the history data are used as the input combined with the weather forecast data, and in the parallel structure, historical data and weather forecast data are used as two separate inputs to the model. The result shows that the parallel structure has higher prediction accuracy than the common one.en_US
dcterms.extentviii, 77 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2022en_US
dcterms.educationalLevelM.Eng.en_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHElectric power systemsen_US
dcterms.LCSHSystem analysisen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/12115