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Application of supervised learning methods to better predict building energy performance

Alaaeddine, Rima and Wu, Song (2017) Application of supervised learning methods to better predict building energy performance. In: International Conference on Sustainable Futures 2017, 26th-27th November 2017, Sitra, Bahrain.

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Abstract

Building energy consumption is shaped by a variety of factors which prompts a
challenge of accurately predicting the building energy performance. Research findings
disclosed a significant gap between the building’s predicted and actual energy performance.
One of the key factors behind this gap is the occupant’s behavior during operation which
includes a set of dependent and independent parameters generating a greater level of
uncertainties. To accurately estimate the energy performance, we need to quantify the
impact of any observed parameters and further detect its correlation with other parameters.
Human behaviors are complex and quantifying the impact of all its interconnected
parameters can be error prone and costly.
To minimize the performance gap, more scalable and accurate prediction approaches, such
as supervised machine learning methods, should be considered.
This paper is devoted to investigate the most commonly used supervised learning methods
which, when intertwined with conventional building energy performance prediction model,
could potentially provide more accurate and reliable estimates. The paper will pinpoint the
best use of each studied method in the relation to energy prediction in general and
occupant’s behavior in specific and how it can be implemented to better predict building
energy performance.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Energy performance, Supervised Machine Learning, Energy prediction, Occupants’ behavior, Performance gap
Subjects: T Technology > T Technology (General)
T Technology > TH Building construction
Schools: School of Art, Design and Architecture
Related URLs:
Depositing User: Sally Hughes
Date Deposited: 19 Dec 2017 11:58
Last Modified: 26 Mar 2018 16:15
URI: http://eprints.hud.ac.uk/id/eprint/34107

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