Artificial intelligence implementation framework development for building energy saving. (1st September 2020)

• Record Type: Journal Article
• Authors: Lee, Dasheng; Huang, Hsu‐Yao; Lee, Wen‐Shing; Liu, Yinghan
• Is Part Of: International journal of energy research. Volume 44:Number 14(2020)
• Page Start: 11908
• Page End: 11929
• Publication Date: 2020-09-01
• Subjects: artificial intelligence (AI) -- artificial intelligence implementation framework (AIif) -- building energy saving -- equipment‐level control -- facility‐level control; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5839
• Languages: English
• ISSNs: 0363-907X

Abstract

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. Abstract : A novel implementation framework was developed to integrate five‐category artificial intelligence (AI) control tools for building energy conservation. 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 in a real building. The unmet demands of AI studies were satisfied, and an approach was established to fill the gap of practical applications.