Predicting fuel consumption for commercial building with machine learning algorithms

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Publication Type pre-print
School or College College of Engineering
Department Mechanical Engineering
Creator Rahman, Aowabin
Other Author Smith, Amanda D.
Title Predicting fuel consumption for commercial building with machine learning algorithms
Date 2017-08
Description This paper presents a modeling framework that uses machine learning algorithms to make longterm, i.e. one year-ahead predictions, of fuel consumption in multiple types of commercial prototype buildings at one-hour resolutions. Weather and schedule variables were used as model inputs, and the hourly fuel consumption simulated with EnergyPlus provided target values. The data was partitioned on a monthly basis, and a feature selection method was incorporated as part of the model to select the best subset of input variables for a given month. Neural networks (NN) and Gaussian process (GP) regression were shown to perform better than multivariate linear regression and ridge regression, and as such, were included as part of the model. The modeling framework was applied to make predictions about fuel consumption in a small office, supermarket, and restaurant in multiple climate zone. It was shown that for all climate zones for all months, the maximum errors pertaining to one year-ahead forecasts of fuel consumption made by the ML model are 15.7 MJ (14,880 Btu), 284.3 MJ (268,516 Btu) and 74.0 MJ (70,138 Btu) respectively. The methods and results from this study can be used to estimate on-site fuel consumption and emissions from; buildings, thereby enabling improved decisions pertaining to building efficiency with respect to fuel use.
Type Text
Publisher Elsevier
Journal Title Energy and Building
Volume 152
First Page 341
Last Page 358
Subject Building energy modeling; Machine learning; Prediction; Heating load; Data-driven modeling
Language eng
Rights Management (c) Aowabin Rahman, Amanda D. Smith
Format Medium application/pdf
ARK ark:/87278/s6m947q1
Setname ir_uspace
Date Created 2018-03-06
Date Modified 2021-05-06
ID 1300868
Reference URL
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