In the US, buildings consume approximately 40% of the entire country's energy consumption [5; 6], and almost 40% of energy is used in residential and commercial buildings in Europe [7]. Buildings consumed 284 TWh of electricity in 2007, accounting for 65% of the total electricity usage in France (434 TWh). A total of 404 million tons of CO\({}_{2}\) were emitted, which was 22.6% of the total emissions [8]. As a result of global warming trends and rising atmospheric temperatures, the heating demand for global residential buildings will decrease by 34% in the year 2100, while cooling demands will increase by 72% [9]. Due to rising energy demands, the industrial revolution has brought with it a slew of new issues. This phenomenon fosters the development of more resource-efficient control approaches. The building sector has a huge potential to mitigate energy demand using intelligent energy management systems (IEMS) [10] and the concept of the internet of energy (IoE). The IoE combines features of a smart grid and the internet of things (IoT). The IoT refers to internet-based architecture in two ways: communication allows the system to be monitored and controlled in real time via cloud computing or another internet service [9; 10; 11]. It is proven that the potential of IoE-based building energy management system BEMS will enhance the performance of future building energy utilization [9]. The key idea of BEMS is monitoring and controlling the energy consumption in buildings with the aim of reducing emissions. The design of BEMS has taken into account factors such as efficiency, scalability, robustness, flexibility, and an ability to sense the environment and make decisions autonomously [12]. A structure of an IEMS is shown in Figure 1. The IEMS consists of an optimization controller that acts like a center controller and interfaces with the operating browser via communication protocols. In practice, the user interface allows interaction seamlessly with connected devices using the same operating browser. Weather information is also taken into account for forecasting energy consumption and generation for the day ahead. Moreover, renewable energy resources (RER), energy storage, and a standby generator for emergency purposes are also considered to reduce the dependency on the grid and compensate for the peak hour and optimal load scheduling. At present, electric vehicles (EVs) are also included, and an IEMS ensures the optimal charging and discharging of vehicle-to-grid (V2G) and G2V during peak and off-peak hours. Furthermore, by educating consumers about the concept of being prosumers, excess RER generation can be sold to the grid with both the utility provider's and the consumer's consent. Several significant articles have been published on BEMS. [PERSON] [13] and [PERSON] [14] discussed artificial intelligence (AI) in demand-side management to compromise energy cost and occupant comfort. However, the authors did not provide a pro and con outline of controllers and optimizations. [PERSON] [15] focused on the consideration of human comforts and intelligent controls, whereas the authors did not discuss the objectives and constraints related to all air-indexed parameters. [PERSON] [16] overviewed the optimization control in building heating, ventilation and air condition HVAC systems to demand-side management (DSM) and also focused on occupants' comfort. However, the authors did not discuss reducing heating and cooling load demand to increase energy efficiency using both passive and active methods. [PERSON] [17] demonstrated building energy efficiency by regulating optimal loads while improving the building envelope in existing buildings. The authors, however, did not provide an outline of strategies for the energy-efficient retrofitting of both existing and new buildings, which could lead to zero energy building (ZEB).