Deep learning techniques for electrical load forecasting

Simple item page
dc.contributor.advisorMacIsaac, Dawn
dc.contributor.advisorCardenas, Julian
dc.contributor.authorOlugbenga, Tolulope Oluwaseun
dc.date.accessioned2023-03-01T16:39:21Z
dc.date.available2023-03-01T16:39:21Z
dc.date.issued2022
dc.date.updated2023-03-01T15:02:57Z
dc.description.abstractLoad forecasting is critical for power system operators to maintain a safe and efficient network. Load forecasting contributes to the supply-demand balance by ensuring that consumers receive adequate energy. Load aggregators, power marketers, and independent system operators can all benefit from load forecasting. Over-forecasting leads to excess production and waste of resources. An unexpectedly high load results in a power outage. Both scenarios result in inefficient generation scheduling and technical difficulties for the operator. It is not simple to create a forecasting model for a specific power network. Statistical and machine-learning techniques have been used in load forecasting. Deep learning techniques have recently gained popularity due to their improved ability to interpret complex data relationships. The purpose of this study was to compare deep learning forecasting techniques to some conventional forecasting techniques used by utilities to see if deep learning can better meet their needs.
dc.description.copyright© Tolulope Oluwaseun Olugbenga, 2022
dc.formattext/xml
dc.format.extentxvi, 118 pages
dc.format.mediumelectronic
dc.identifier.oclc(OCoLC)1418945614en
dc.identifier.otherThesis 10973en
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/14278
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.subject.lcshSupply and demand.en
dc.subject.lcshDeep learning (Machine learning)en
dc.subject.lcshElectric power-plants--Load.en
dc.titleDeep learning techniques for electrical load forecasting
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.fullnameMaster of Science in Engineering
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.Sc.E.

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
item.pdf
Size:
6.68 MB
Format:
Adobe Portable Document Format
Download

Collections

Open Theses & Dissertations