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: | 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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