Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159025
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDu, Zhenyuanen_US
dc.date.accessioned2022-06-05T12:04:42Z-
dc.date.available2022-06-05T12:04:42Z-
dc.date.issued2022-
dc.identifier.citationDu, Z. (2022). Probabilistic forecasting of residential load power for smart home. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159025en_US
dc.identifier.urihttps://hdl.handle.net/10356/159025-
dc.description.abstractDue to the stochastic nature of occupants’ behaviors, forecasting individual household-level residential load for smart home has been a challenging problem. This paper proposes a probabilistic residential load power forecasting method for smart home considering three aspects: deep-learning-based point-forecasting of residential load, prediction intervals to estimate the load uncertainties, and non-intrusive load monitoring (NILM).en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleProbabilistic forecasting of residential load power for smart homeen_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 (Computer Control and Automation)en_US
dc.contributor.supervisoremailxuyan@ntu.edu.sgen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
[DU ZHENYUAN(G2103702E)].pdf
  Restricted Access
1.94 MBAdobe PDFView/Open

Page view(s)

27
Updated on Aug 13, 2022

Download(s)

1
Updated on Aug 13, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.