Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141314
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dc.contributor.authorAo, Boyuen_US
dc.date.accessioned2020-06-07T13:33:32Z-
dc.date.available2020-06-07T13:33:32Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/141314-
dc.description.abstractForecasting is one of the few requirements for a successful energy management system applications. In a data-driven analytics application such as forecasting, data pre-processing, manual or automatic data features extraction and machine learning techniques are some of the main components requires in a data science related works. This project aims to develop a AI method for electricity load forecasting at the household level.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleHousehold load forecastingen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorXu Yanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Power Engineering)en_US
dc.contributor.supervisor2Xu Yanen_US
dc.contributor.supervisoremailxuyan@ntu.edu.sgen_US
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