# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 3 Smart Building Monitoring Applications
### Energy Management  
Energy efficiency optimization is driven by good business practices as well as by government regulations. The building sector represents a significant portion of the world's total energy consumption. Based on a survey by the International Energy Agency (IEA) [2], the global building sector consumes 42 percent of the total generated electricity. Building energy monitoring and management systems ensure energy efficiency by overseeing energy usage through the collection and analysis of real-time data, whether gathered manually or from sensor networks. Therefore, [PERSON] et al. [28] argue that it is possible to forecast the energy utilization trends for different buildings and make realistic estimations. To establish an efficient energy management approach, it is a crucial step to monitor related energy-consuming resources such as heat, electric power, water, and others. This process relies on measuring both the static parameters that remain constant over the building's lifespan (such as building materials, orientation, and placement) and dynamic parameters, which are variable over time (including weather data, occupancy characteristics, energy use, and water use) [29]. When monitoring electricity consumption, techniques can be categorized into intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). The NILM method is implemented with a low level of intrusiveness at a single point of measurement, typically at the point where the building is connected to the electrical grid. On the other hand, The ILM method requires a higher level of intrusion into the energy system compared to NILM into the energy system, as it involves the addition of meters or sensors. In a 2014 survey article, ILM is divided into three levels based on their level of intrusiveness: submeter level (at the circuit breaker), plug level, and embedded level [30]. However, in recent years, the definition of ILM has changed. Based on recent articles, ILM implies that collected energy data do not require data disaggregation to distinguish various appliances [31; 32; 33] as in the case of NILM. NILM relies on the concept of a unique energy utilization pattern for each load and its goal is to filter out the energy consumption of individual equipment from the total power usage [31]. ILM gives finer details about appliance energy consumption; however, the installation process is more complex compared to NILM since many energy meters have to be set up depending on appliance count. Costs of an ILM system increase linearly as the sensor count grows. In NILM, it is feasible to derive characteristics of individual appliance energy consumption through the analysis of the whole consumption data. Then, by utilizing appliance recognition algorithms [33; 34], it is possible to disaggregate appliance energy consumption data. Figure 2 shows the required steps of NILM signal processing for appliance energy consumption monitoring. To ease the process of processing energy consumption signals for energy consumption devices, they can first be segregated into four groups depending on their energy consumption patterns [33]:  
1. Appliances with binary power states (ON/OFF);
2. Appliances with multiple and finite power states;3. Appliances with multiple power states, including infinite intermediate states;
4. Appliances with a constant power consumption that stays ON for days or weeks. Given that each appliance exhibits a unique energy consumption pattern, a \"signature\" can be derived for each one. This \"signature\" can be understood as a compilation of the specific electrical characteristics associated with a particular load. The notable drawback of methods where energy signal disaggregation has to be performed is its inability to monitor all types of loads, such as loads with continuously variable power consumption and loads with constant power consumption during a long period. Currently, intrusive energy monitoring is considered as the most viable and efficient method on the market [34].