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My goal is to enrich our community's quality of life through meaningful engineering innovation and sustainability development. Therefore, my research focus is around renewables and sustainable energy future.
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.
Le Truong; Ka Chow; Rungsimun Luevisadpaibul; Gokul Thirunavukkarasu; Mehdi Seyedmahmoudian; Ben Horan; Saad Mekhilef; Alex Stojcevski. Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches. Applied Sciences 2021, 11, 2229 .
AMA StyleLe Truong, Ka Chow, Rungsimun Luevisadpaibul, Gokul Thirunavukkarasu, Mehdi Seyedmahmoudian, Ben Horan, Saad Mekhilef, Alex Stojcevski. Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches. Applied Sciences. 2021; 11 (5):2229.
Chicago/Turabian StyleLe Truong; Ka Chow; Rungsimun Luevisadpaibul; Gokul Thirunavukkarasu; Mehdi Seyedmahmoudian; Ben Horan; Saad Mekhilef; Alex Stojcevski. 2021. "Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches." Applied Sciences 11, no. 5: 2229.