Journal
ENERGY AND BUILDINGS
Volume 137, Issue -, Pages 13-26Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2016.12.016
Keywords
Building thermal loads; Input selection; Predictive model
Funding
- Irish Research Council
- United Technologies Research Centre (UTRC)
- Electricity Research Centre, University College Dublin
- Commission for Energy Regulation
- Bord Gis Energy
- Bord na Mona Energy
- Cylon Controls
- EirGrid
- Electric Ireland
- Energia
- EPRI
- ESB International
- ESB Networks
- Gaelectric
- Intel
- SSE Renewables
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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 Energy Plus 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. (C) 2016 Elsevier B.V. All rights reserved.
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