## 6 Energy Management
### Building Energy System Modelling  
Building energy systems (BES) support the operations within a house, such as space heating, data transmitting, and operating appliances. BES regulates the energy consumption of the buildings by employing physical equipment or machinery, or even exerting a procedure that contains an integration [113]. In order to manage, predict, and optimize the operations of these systems and houses, numerical models have been created to simulate the energy consumption. Regardless of various structures that are proposed as models that simulate the buildings energy system, an energy system model contains three modules including input parameters, output parameters, and the system itself [114], this is demonstrated in Figure 3.  
As Figure 3 indicates, the input variables known as regressor or forcing variables operate within the building's energy system. For instance, a thermostat set point is considered as a controllable input variable. In contrast, solar radiation, outdoor air temperature, and wind speed are acknowledged as variables with uncontrollable variables, these can only be predicted by suitable techniques. For an energy system within a building, room air space temperature and humidity are regarded as output variables that explain the response of the building's energy system to input variables. Output variables of a BES model in certain occasions can be the net energy consumption or the energy utilization itself [115]. A BES modelling architecture is mainly categorized as: Forward approach and data-driven approach [115; 116; 117]. A BES model can be developed utilizing a forward procedure. This comprises the forecast of output variables from a comprehensive architecture and parameter of the model conformed to a set of input variables. Models that are developed with execution of this approach are recognized as white-box models; these are extremely precise since the energy transmission procedures are mapped into the development of the BES modelling structure. Augmented trends of integrity in computing schemes enabled the development of precise BES models which indicates the fact that BES does not rely on constructive development and preserves time and energy. Forward modelling of BES is carried out within the preliminary design and analysis phase. Data driven approaches, which can be further categorized into grey- and black-box models, empower development of BES model by comprehending both input and output data. This type data is either already known or attained within performed evaluations throughout experimental procedures. In this scheme, the BES model is structured utilizing a single-variable or multi-variable regression analysis performed among quantified output variables of energy usage, occupancy, solar and wind data, etc. Regression models are either statistical or relies on primary engineering formulation of energy consumption in buildings. Long-term simulations require time-consuming and prohibitive arrangements to capture the variables within the simulation duration. In this case artificial learning methods such as Artificial Neural Networks (ANN) and Fourier series are employed to estimate the parameters of the underlying model. However, in most cases the identified BES model parameters indicate minor physical significances which decrements the amount of data to be measured using the artificial learning or searching techniques that preserves significant amount of time and memory. Figure 3: Structure of a building energy system [115]. Moreover, BES model parameters are evaluated based on genuine performance data and data driven models happen to be precise considering the computational intelligence and power. To formulate a physical model to demonstrate the architecture, the physical arrangement of the building, or HVAC&R equipment or system, a grey box scheme is utilized. This scheme recognizes significant parameters and accumulates physical parameters and specifications through statistical analysis [116].