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- Title
Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches.
- Authors
Truong, Le Hoai My; Chow, Ka Ho Karl; Luevisadpaibul, Rungsimun; Thirunavukkarasu, Gokul Sidarth; Seyedmahmoudian, Mehdi; Horan, Ben; Mekhilef, Saad; Stojcevski, Alex; Bedon, Chiara
- Abstract
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.
- Subjects
HOME energy use; OCCUPANCY rates; MACHINE learning; STANDARD deviations; ARTIFICIAL neural networks; LOAD forecasting (Electric power systems)
- Publication
Applied Sciences (2076-3417), 2021, Vol 11, Issue 5, p2229
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app11052229