horan-accuratepredictionof-2021.pdf (2.92 MB)
Accurate prediction of hourly energy consumption in a residential building based on the occupancy rate using machine learning approaches
journal contribution
posted on 2021-03-01, 00:00 authored by Le Hoai My Truong, Ka Ho Karl Chow, Rungsimun Luevisadpaibul, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Ben HoranBen Horan, Saad Mekhilef, Alex StojcevskiIn 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.
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Journal
Applied SciencesVolume
11Issue
5Publisher
MDPI AGLocation
Basel, SwitzerlandPublisher DOI
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eISSN
2076-3417Language
engNotes
This article belongs to the Special Issue Sustainable Built Environments in 21st CenturyPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2021, The AuthorsUsage metrics
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No categories selectedKeywords
deep learningenergy management systemsload forecastingmachine learning and microgridsScience & TechnologyPhysical SciencesTechnologyChemistry, MultidisciplinaryEngineering, MultidisciplinaryMaterials Science, MultidisciplinaryPhysics, AppliedChemistryEngineeringMaterials SciencePhysicsREGRESSION-ANALYSISMANAGEMENTOPERATIONSYSTEM
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