Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

Ahmad, Muhammad ORCID: https://orcid.org/0000-0002-7269-4369, Mourshed, Monjur ORCID: https://orcid.org/0000-0001-8347-1366 and Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 2017. Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 147 , pp. 77-89. 10.1016/j.enbuild.2017.04.038

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Abstract

Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction – for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	HVAC systems; Artificial Neural networks; Random Forest; Decision trees; Ensemble algorithms; Energy efficiency; Data mining
Publisher:	Elsevier
ISSN:	0378-7788
Funders:	European Research Council
Date of First Compliant Deposit:	18 May 2017
Date of Acceptance:	6 April 2016
Last Modified:	08 May 2023 18:42
URI:	https://orca.cardiff.ac.uk/id/eprint/100253

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