TY - GEN
T1 - Machine Learning-Based Occupant Energy Use Behavior Optimization
AU - Amasyali, Kadir
AU - El-Gohary, Nora
N1 - Funding Information:
This publication was made possible by NPRP Grant #6-1370-2-552 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.
Publisher Copyright:
© ASCE.
PY - 2018
Y1 - 2018
N2 - Buildings account for a large percentage of the energy consumption in the United States. A significant portion of this energy is used to achieve thermal comfort in buildings. Recent studies showed that occupant behavior is one of the most influential factors that affect building energy consumption. A significant amount of energy, therefore, can be saved through improving the behavior of occupants. Although a large body of studies have been conducted to better understand and improve occupant behavior, there is still a lack of research attention to identifying the occupant behavior that would achieve both reduction in energy consumption and improvement in occupant comfort. To address this need, this paper presents a machine-learning-based approach (1) to model the impact of occupant behavior on building cooling energy consumption, and (2) optimize a set of occupant-behavior-related variables for reduced cooling energy consumption while maintaining occupant thermal comfort. The research methodology included three primary phases. First, a set of buildings were modelled and simulated in EnergyPlus, with different values for occupant-behavior-related variables. Second, learning from the simulation-generated data, support vector machine (SVM)-based occupant-behavior-sensitive models for predicting hourly building cooling energy consumption, operative temperature, and relative humidity were developed. Third, using genetic algorithm, a preliminary hourly occupant-behavior optimization was conducted to determine the optimal values for occupant-behavior-related variables for achieving minimum hourly cooling energy consumption levels while maintaining occupant thermal comfort. The results showed that the proposed machine-learning-based approach can be successful in optimizing occupant behavior.
AB - Buildings account for a large percentage of the energy consumption in the United States. A significant portion of this energy is used to achieve thermal comfort in buildings. Recent studies showed that occupant behavior is one of the most influential factors that affect building energy consumption. A significant amount of energy, therefore, can be saved through improving the behavior of occupants. Although a large body of studies have been conducted to better understand and improve occupant behavior, there is still a lack of research attention to identifying the occupant behavior that would achieve both reduction in energy consumption and improvement in occupant comfort. To address this need, this paper presents a machine-learning-based approach (1) to model the impact of occupant behavior on building cooling energy consumption, and (2) optimize a set of occupant-behavior-related variables for reduced cooling energy consumption while maintaining occupant thermal comfort. The research methodology included three primary phases. First, a set of buildings were modelled and simulated in EnergyPlus, with different values for occupant-behavior-related variables. Second, learning from the simulation-generated data, support vector machine (SVM)-based occupant-behavior-sensitive models for predicting hourly building cooling energy consumption, operative temperature, and relative humidity were developed. Third, using genetic algorithm, a preliminary hourly occupant-behavior optimization was conducted to determine the optimal values for occupant-behavior-related variables for achieving minimum hourly cooling energy consumption levels while maintaining occupant thermal comfort. The results showed that the proposed machine-learning-based approach can be successful in optimizing occupant behavior.
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U2 - 10.1061/9780784481301.038
DO - 10.1061/9780784481301.038
M3 - Conference contribution
AN - SCOPUS:85048708285
T3 - Construction Research Congress 2018: Sustainable Design and Construction and Education - Selected Papers from the Construction Research Congress 2018
SP - 379
EP - 389
BT - Construction Research Congress 2018
A2 - Lee, Yongcheol
A2 - Harris, Rebecca
A2 - Wang, Chao
A2 - Harper, Christofer
A2 - Berryman, Charles
PB - American Society of Civil Engineers
T2 - Construction Research Congress 2018: Sustainable Design and Construction and Education, CRC 2018
Y2 - 2 April 2018 through 4 April 2018
ER -