# Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
## 2 Application of AI to Smart and Energy-Efficient Buildings  
A recent survey on the energy consumption in buildings revealed their high levels of energy consumption. In a global study conducted by the International Energy Agency, it was shown that buildings account for 71% of the total electricity and 39% of the final energy consumption in an urban area; furthermore, buildings are responsible for close to 40% of the total carbon dioxide emissions in urban areas [31]. The most promising application of AI in urban energy systems is the advent of smart buildings. Smart buildings are equipped with numerous sensors, subsystems, and actuators, and their many advanced and smart automation monitoring and control tools can be utilized for energy-saving purposes. These reductions in energy consumption are accompanied by simultaneous reductions in greenhouse gas emissions. It was shown that the potential impact of smart buildings is in urban areas toward protecting the environment, reducing the running costs of buildings, and conserving energy [32; 33; 34; 35]. The smart building is a concept representative of the modern infrastructure. It includes automated control systems and utilizes data to optimize the building's performance and the level of comfort experienced by its inhabitants [36]. It provides a cyber-physical system (CPS) that integrates the cyber world; it also combines sensing devices, controllers, and metering components with the physical world, which includes various electric appliances and electronic devices (Figure 1). The smart building concept is based on the correct assumption that free interactions between both worlds yield advantages. As technology advances, the presence of AI technologies in smart buildings becomes more pronounced. The joint development of ever smaller, more energy-efficient sensors and communication procedures with parallel advances in hardware and software technologies provides simpler possibilities for evaluating, monitoring, and interacting with the environment [38]. Efficiency is constantly being improved with the lower operating and maintenance costs and the reductions in energy consumption as well as with parallel improvements in the comfort level and safety conditions for the inhabitants of the buildings [39]. This concept, known as the building management system (BMS), hints at the potential of AI to greatly enhance in the future not only the efficiency of buildings but also the experience of living in them. Smart buildings and smart homes are essential components of smart grids. Demand side management (DSM) plays a crucial role in the concept of smart grid (SG) by developing diverse and flexible strategies to conserve the stability of the grid. The purpose of DSM is to overcome the challenges of the traditional grid by strategically planning and contributing to the control and management of demand loads of electricity [40,41]. AI features strongly in automated energy systems that integrate demand response programs (DRPs) and learn from human behavior to reduce user discomfort and to determine the required level of human-controller interaction [42]. DRPs can be viewed as institution-led efforts to reduce the energy demand by inducing changes in energy consumption in response to changes in tariffs or incentive payments. The application of DRPs to smart grids equipped with renewable energy resources has been studied extensively [43, 44, 45]. As shown in Figure 2, DRPs can be classified into time-based rate (TBR) programs and incentive-based programs (IBPs). TBR programs are based mostly on dynamic pricing, which aims to smooth the load by varying consumer electricity prices based on the time of day. These programs are designed to encourage consumers to react sensitively to the reduced rate of electricity at certain times of the day.