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Accurate prediction of hourly energy consumption in a residential building based on the occupancy rate using machine learning approaches

Truong, Le Hoai My, Chow, Karl Ka Ho, Luevisadpaibul, Rungsimun, Thirunavukkarasu, Gokul Sidarth, Seyedmahmoudian, Mehdi, Horan, Ben, Mekhilef, Saad and Stojcevski, Alex 2021, Accurate prediction of hourly energy consumption in a residential building based on the occupancy rate using machine learning approaches, Applied Sciences, vol. 11, no. 5, doi: 10.3390/app11052229.

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Title Accurate prediction of hourly energy consumption in a residential building based on the occupancy rate using machine learning approaches
Author(s) Truong, Le Hoai My
Chow, Karl Ka Ho
Luevisadpaibul, Rungsimun
Thirunavukkarasu, Gokul Sidarth
Seyedmahmoudian, Mehdi
Horan, BenORCID iD for Horan, Ben orcid.org/0000-0002-6723-259X
Mekhilef, Saad
Stojcevski, Alex
Journal name Applied Sciences
Volume number 11
Issue number 5
Total pages 19
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021-03-01
ISSN 2076-3417
Keyword(s) deep learning
energy management systems
load forecasting
machine learning and microgrids
Summary In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residential load profiles helps energy providers and utility companies develop an optimal generation schedule to address the demand. Initially, a comprehensive multi-criteria analysis of different machine learning approaches used in energy consumption predictions was carried out. Later, a predictive micro-grid model was formulated to synthetically generate the stochastic load profiles considering occupancy rate as the critical input. Finally, the synthetically generated data were used to train the proposed eight-layer deep neural network-based model and evaluated using root mean square error and coefficient of determination as metrics. Observations from the results indicated that the proposed energy prediction algorithm yielded a coefficient of determination of 97.5% and a significantly low root mean square error of 111 Watts, thereby outperforming the other baseline approaches, such as extreme gradient boost, multiple linear regression, and simple/shallow artificial neural network.
Notes This article belongs to the Special Issue Sustainable Built Environments in 21st Century
Language eng
DOI 10.3390/app11052229
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2021, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30149271

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.