Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on...
Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method
About this item
Full title
Author / Creator
Lee, Jaehyun , Kim, Jinho and Ko, Woong
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
Subjects
More information
Scope and Contents
Contents
Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load fore...
Alternative Titles
Full title
Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_00003cc686e04527b2a6f9252fc3b14a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_00003cc686e04527b2a6f9252fc3b14a
Other Identifiers
ISSN
2076-3417
E-ISSN
2076-3417
DOI
10.3390/app9061231
