Input Variable Selection for Thermal Load Predictive Models of Commercial Buildings
|Title:||Input Variable Selection for Thermal Load Predictive Models of Commercial Buildings||Authors:||Kapetanakis, Dimitrios-Stavros; Mangina, Eleni; Finn, Donal||Permanent link:||http://hdl.handle.net/10197/11547||Date:||15-Feb-2017||Online since:||2020-09-08T14:04:16Z||Abstract:||Forecasting of commercial building thermal loads can be achieved using data from Building Energy Management (BEM) systems. 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 the selection of appropriate input variables, for data-driven predictive models, from wider datasets obtained from BEM systems sensors, as well as from weather data. To address the lack of available complete datasets from actual commercial buildings BEM systems, detailed representation of reference buildings using EnergyPlus were implemented. Different types of commercial buildings in various climates are examined to investigate the existence of patterns in the selection of input variables. Data analysis of the simulated results is used to detect the correlation between thermal loads and possible input variables. The selection process is validated by comparing the performance of predictive models when the full or the pre-selected set of variables is introduced as inputs.||Funding Details:||Irish Research Council
University College Dublin
|metadata.dc.description.othersponsorship:||United Technologies Research Centre (UTRC)||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Energy and Buildings||Volume:||137||Start page:||13||End page:||26||Copyright (published version):||2016 Elsevier||Keywords:||Building thermal loads; Input selection; Predictive model; Artificial neural networks; Support vector machine; Energy consumption; Regression models; Office buildings; Cooling load||DOI:||10.1016/j.enbuild.2016.12.016||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mechanical & Materials Engineering Research Collection|
Computer Science Research Collection
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