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Artificial intelligence implementation framework development for building energy saving
Author(s) -
Lee Dasheng,
Huang HsuYao,
Lee WenShing,
Liu Yinghan
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5839
Subject(s) - energy conservation , energy consumption , building automation , efficient energy use , engineering , control (management) , automation , energy (signal processing) , architectural engineering , computer science , artificial intelligence , electrical engineering , mechanical engineering , statistics , physics , mathematics , thermodynamics
Summary In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five‐category AI control tools to execute three‐level building energy conservation; the three levels consist of equipment‐level control, facility‐level control, and whole building energy saving. Energy‐saving effects were tested in a real building. The complex three‐floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed.