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Forecasting heat load for smart district heating systems: A machine learning approach

contributor authorIdowu, Samuel
contributor authorSaguna, Saguna
contributor authorAhlund, Christer
contributor authorSchelen, Olov
date accessioned2020-03-12T22:24:39Z
date available2020-03-12T22:24:39Z
date issued2014
identifier other7007705.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1082064?show=full
formatgeneral
languageEnglish
publisherIEEE
titleForecasting heat load for smart district heating systems: A machine learning approach
typeConference Paper
contenttypeMetadata Only
identifier padid8218612
subject keywordsimage convertors
subject keywordsn image processing
subject keywordsn inverse problems
subject keywordsn laser beam effects
subject keywordsn numerical analysis
subject keywordsn opacity
subject keywordsn optical focusing
subject keywordsn absorbing medium
subject keywordsn contrast inversion process
subject keywordsn focusing laser
subject keywordsn illuminating beam
subject keywordsn initial beam intensity
subject keywordsn inverse image conversion
subject keywordsn inverse image formation
subject keywordsn numerical simulation
subject keywordsn object size
subject keywordsn opaque objects
subject keywordsn small-scale objects
subject keywordsn transmitted radiation
subject keywordsn Computational modeling
subject keywordsn Heat sinks
subject keywordsn Inverse
identifier doi10.1109/LO.2014.6886497
journal titlemart Grid Communications (SmartGridComm), 2014 IEEE International Conference on
filesize272808
citations0


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