Next Article in Journal
Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate
Previous Article in Journal
A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm
Article Menu

Export Article

Open AccessArticle
Energies 2018, 11(12), 3408; https://doi.org/10.3390/en11123408

Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption

1
BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
2
Commissariat á l’énergie atomique et aux énergies alternatives (CEA), CEA Tech en Région (CTREG), Département Grand Ouest (DGDO), 44340 Bouguenais, France
*
Author to whom correspondence should be addressed.
Received: 1 October 2018 / Revised: 19 November 2018 / Accepted: 27 November 2018 / Published: 5 December 2018
Full-Text   |   PDF [1405 KB, uploaded 5 December 2018]   |  

Abstract

Predictive analytics play a significant role in ensuring optimal and secure operation of power systems, reducing energy consumption, detecting fault and diagnosis, and improving grid resilience. However, due to system nonlinearities, delay, and complexity of the problem because of many influencing factors (e.g., climate, occupants’ behaviour, occupancy pattern, building type), it is a challenging task to get accurate energy consumption prediction. This paper investigates the accuracy and generalisation capabilities of deep highway networks (DHN) and extremely randomized trees (ET) for predicting hourly heating, ventilation and air conditioning (HVAC) energy consumption of a hotel building. Their performance was compared with support vector regression (SVR), a most widely used supervised machine learning algorithm. Results showed that both ET and DHN models marginally outperform the SVR algorithm. The paper also details the impact of increasing the deep highway network’s complexity on its performance. The paper concludes that all developed models are equally applicable for predicting hourly HVAC energy consumption. Possible reasons for the minimum impact of DHN complexity and future research work are also highlighted in the paper. View Full-Text
Keywords: HVAC systems; deep learning; energy efficiency; tree-based ensemble algorithms; machine learning; support vector regression HVAC systems; deep learning; energy efficiency; tree-based ensemble algorithms; machine learning; support vector regression
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ahmad, M.W.; Mouraud, A.; Rezgui, Y.; Mourshed, M. Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption. Energies 2018, 11, 3408.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top