Artificial intelligence implementation framework development for building energy saving

Abstract

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. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

“…Furthermore, machine‐learning methods are part of the study in seven publications. Five of these papers were just published in 2020 and address topics such as deep reinforcement learning and adaptive neuro‐fuzzy inference systems 29,38,45,58,59,97 . This trend may be an indication of the future importance of machine learning in EMS.…”

“…Only 10 papers focus on EMS in non-residential buildings, such as hotels or office buildings. 4,38,59,62,68,72,84,91,97,98 Photovoltaic systems and battery storage are also frequently considered, with 64% and 56% respectively. The sector coupling of electricity and heat by means of heat pumps or CHP units is less relevant and is dealt with in only 13% and 5% of the literature.…”

“…Of the researched scientific publications, 98 papers are accessible and can be evaluated to determine the current state of research 3‐100 . Only these 98 papers can be used to examine the current state of research.…”

“…The high share of household appliances in the design of HEMS is based on the fact that 88 out of 98 papers focus on EMS of residential buildings and households. Only 10 papers focus on EMS in non‐residential buildings, such as hotels or office buildings 4,38,59,62,68,72,84,91,97,98 . Photovoltaic systems and battery storage are also frequently considered, with 64% and 56% respectively.…”

Optimal energy m anagement of s mart b uildings under cyber attack

“…Authors 9 have proposed a multi-agent-based distribution system to reduce energy consumption in the intelligent community system. Artificial intelligent based 10 tools are used to manage the three-level building energy management. The authors 11 propose optimal power management between PV and battery storage in SHs.…”