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Random forests and artificial neural network for predicting daylight illuminance and energy consumption

Ahmad, Muhammad, Hippolyte, Jean-Laurent, Mourshed, Monjur and Rezgui, Yacine 2017. Random forests and artificial neural network for predicting daylight illuminance and energy consumption. Presented at: Building Simulation 2017: 15th Conference of International Building Performance Simulation Association, San Francisco, CA, USA, 7-9 August 2017. Published in: Barnaby, Charles S. and Wetter, Michael eds. Building Simulation 2017. Proceedings of the International Building Performance Simulation Association IBPSA, pp. 1949-1955.

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Abstract

Predicting energy consumption and daylight illuminance plays an important part in building lighting control strategies. The use of simplified or data-driven methods is often preferred where a fast response is needed e.g. as a performance evaluation engine for advanced real-time control and optimization applications. In this paper we developed and then compared the performance of the widely-used Artificial Neural Network (ANN) with Random Forest (RF), a recently developed ensemble-based algorithm. The target application was predicting the hourly energy consumption and daylight illuminance values of a classroom in Cardiff, UK. Overall, RF performed better than ANN for predicting daylight illuminance; with coefficients of determination (R^2) of 0.9881 and 0.9799 respectively. On the energy consumption testing dataset, ANN performed marginally better than RF with R^2 values of 0.9973 and 0.9966 respectively. RF performs internal cross-validation and is relatively easy to tune as it has few tuning parameters. The paper also highlighted possible future research directions.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: IBPSA
ISBN: 978-1-7750520-0-5
ISSN: 2522-2708
Funders: European Research Council
Date of First Compliant Deposit: 27 March 2018
Last Modified: 26 Aug 2019 23:14
URI: http://orca.cf.ac.uk/id/eprint/100415

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