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

Muhammad Waseem Ahmad, Anthony Mouraud, Yacine Rezgui and Monjur Mourshed
Additional contact information
Muhammad Waseem Ahmad: BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Anthony Mouraud: Commissariat á l’énergie atomique et aux énergies alternatives (CEA), CEA Tech en Région (CTREG), Département Grand Ouest (DGDO), 44340 Bouguenais, France
Yacine Rezgui: BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Monjur Mourshed: BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK

Energies, 2018, vol. 11, issue 12, 1-21

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.

Keywords: HVAC systems; deep learning; energy efficiency; tree-based ensemble algorithms; machine learning; support vector regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/12/3408/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/12/3408/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:12:p:3408-:d:188132

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().