%0 Conference Proceedings
%T A Short-Term Forecast Approach of Public Buildings’ Power Demands upon Multi-source Data
%+ Tongji University
%A Shan, Shubing
%A Cao, Buyang
%Z Part 3: MAKE Privacy
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Reggio, Italy
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-10410
%P 174-188
%8 2017-08-29
%D 2017
%R 10.1007/978-3-319-66808-6_12
%K Electricity demand
%K Short-term forecast
%K Multi–source data
%K Grey relational analysis
%K Probabilistic graph
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Due to the significant increase of the global electricity demand and the rising number of urban population, the electric consumption in a city has attracted more attentions. Given the fact that public buildings occupy a large proportion of the electric consumption, the accurate prediction of electric consumptions for them is crucial to the rational electricity allocation and supply. This paper studies the possibility of utilizing urban multi-source data such as POI, pedestrian volume etc. to predict buildings’ electric consumptions. Among the multiple datasets, the key influencing factors are extracted to forecast the buildings’ electric power demands by the given probabilistic graphical algorithm named EMG. Our methodology is applied to display the relationships between the factors and forecast the daily electric power demands of nine public buildings including hotels, shopping malls, and office buildings in city of Hangzhou, China over the period of a month. The computational experiments are conducted and the result favors our approach.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-01677140/document
%2 https://inria.hal.science/hal-01677140/file/456304_1_En_12_Chapter.pdf
%L hal-01677140
%U https://inria.hal.science/hal-01677140
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-10410
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9