Journal
6TH INTERNATIONAL BUILDING PHYSICS CONFERENCE (IBPC 2015)
Volume 78, Issue -, Pages 3001-3006Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.egypro.2015.11.704
Keywords
commercial building; thermal loads; input selection; predictive model
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Forecasting of building thermal loads, without the use of simulation software, can be achieved using data from Building Energy Management Systems (BEMS). Experience in building load prediction using historical data has shown that data analysis is a key factor in order to produce accurate results. This paper examines issues related to the selection of appropriate input variables from wider datasets obtained from BEMS sensors. These variables will be introduced to a new data-driven model, which estimates building space loads. Results indicate that ambient temperature and relative humidity along with solar radiation are the predominant variables that should be considered as input variables to the predictive model. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/hy-nc-nd/4.0/).
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