Next Article in Journal
Legal Regulation of Hydrogen in Germany and Ukraine as a Precondition for Energy Partnership and Energy Transition
Next Article in Special Issue
Towards a Blockchain-Based Peer-to-Peer Energy Marketplace
Previous Article in Journal
Water-Cooled Thermoelectric Generators for Improved Net Output Power: A Review
Previous Article in Special Issue
Energy Management Model for HVAC Control Supported by Reinforcement Learning
 
 
Article

Application of Artificial Neural Networks for Virtual Energy Assessment

1
Renewable Energy and Energy Efficiency Group, Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3004, Australia
2
Virginia-Maryland College of Veterinary Medicine, Roanoke, VA 24060, USA
3
Faculty of Business, UQ Business School, Economics & Law, University of Queensland, Brisbane, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Ana-Belén Gil-González
Energies 2021, 14(24), 8330; https://doi.org/10.3390/en14248330
Received: 17 November 2021 / Revised: 30 November 2021 / Accepted: 8 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions. View Full-Text
Keywords: virtual energy assessment; artificial neural network; commercial buildings; energy efficiency; energy saving virtual energy assessment; artificial neural network; commercial buildings; energy efficiency; energy saving
Show Figures

Figure 1

MDPI and ACS Style

Mortazavigazar, A.; Wahba, N.; Newsham, P.; Triharta, M.; Zheng, P.; Chen, T.; Rismanchi, B. Application of Artificial Neural Networks for Virtual Energy Assessment. Energies 2021, 14, 8330. https://doi.org/10.3390/en14248330

AMA Style

Mortazavigazar A, Wahba N, Newsham P, Triharta M, Zheng P, Chen T, Rismanchi B. Application of Artificial Neural Networks for Virtual Energy Assessment. Energies. 2021; 14(24):8330. https://doi.org/10.3390/en14248330

Chicago/Turabian Style

Mortazavigazar, Amir, Nourehan Wahba, Paul Newsham, Maharti Triharta, Pufan Zheng, Tracy Chen, and Behzad Rismanchi. 2021. "Application of Artificial Neural Networks for Virtual Energy Assessment" Energies 14, no. 24: 8330. https://doi.org/10.3390/en14248330

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

Article Access Map by Country/Region

1
Back to TopTop