With the development of artificial intelligence and Internet of Things (IoT) technologies, modern architecture is increasingly becoming intelligent, green, and human-centered. The intelligent retrofitting of existing buildings is an important means of achieving green buildings, efectively reducing energy consumption and carbon emissions. The challenge of building energy eficiency retrofitting lies in the design of retrofitting and maintenance plans based on contemporary information methods, aiming to minimize economic costs and maximize energy savings. Unlike traditional energy system control and scheduling, energy eficiency retrofitting lies on researching artificial intelligence and data mining technologies to make optimal decisions throughout the entire life cycle of a building. This report primarily introduces the latest applications of information theory methods in building retrofitting from four aspects: modeling, optimization, control, and game theory. It analyzes requirements across diferent dimensions of problem, such as single-objective vs. multi-objective, static vs. dynamic, and single-party vs. multi-party participation, and proposes improvements and innovations in intelligent algorithms.
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