## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.1 Energy Management Systems
(4) Local command stage, which sends the aforementioned values to the various parts of the system through an internal home area network (HAN). The proposed system is evaluated by a test bench, including four smart homes, all of which take advantage of the proposed EMS system. A number of wireless sensors are connected with electric devices in order to transfer the data to the EMS. In order to transfer the GEPP, all the homes are connected to a data collector of the grid manager via a safe Internet connection. The gathered data can be used to improve planning. The findings of the system evaluation by a test bench showed that the uncertainty of the power exchanged between the grid and the user falls to 2.88% and also shows a fall of 3.23% in the electricity bills. Moreover, the proposed EMS system is able to perform its difficult task for less than nine minutes. It was suggested to test the proposed system in real smart homes. Besides, other aspects like the health state of the battery, in the EMS formulation, should be considered in the future. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [43] proposed a model for integrating smart devices in a home, which consume their own produced energy, and the architecture model includes IoT nodes, such as edge and fog node, local smart-grid Internet,and the cloud service. It also takes advantage of horizontal and vertical algorithms, both of which are two groups of energy management models. The proposed model performs based on the following steps: (1) The architecture model performance: (1-1) The edge node can use simple AI algorithms acting in the local area and is connected vertically with other layers, it also makes use of horizontal algorithms of control. It handles the sensors and actuators. (1-2) The fog node is more capable in terms of the computational aspect in comparison with the edge node. It maintains the data and analyzes the process of energy management by means of AI algorithms and takes the information internationally. While it is not connected with the local data, unlike the edge node. It makes use of vertical algorithms. It can be in the shape of a PC or a server. (1-3) The smart grid is responsible for managing various nodes, and it is an interface to the cloud service. (1-4) The cloud service is a connection between two smart grids. (2) The computing model performance is as follows: (2-1) The design of all the hardware, software, and communication. (2-2) The flow of the data between nodes like capturing, filtering, processing, and transmitting. (2-3) Learning, including machine learning patterns and AI models. (2-4) The operation includes applying the algorithms on the devices in order to control local facilities. (2-5) Administration of the data network by specific computer resources. Details about both architecture and computing models developed by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [43] includes the following two main components:  
1. proposed architecture model layers, such as (1-1) an edge node is able to do these tasks: taking the data of energy generation and consumption, taking the environmental data, pre-processing and filtering all the data gathered, controlling the performance of actuators, classifying models, predicting models, processing the communication, and implementing various user interfaces. (1-2) the fog node is able to do these tasks: learning prediction and classification models, analyzing, storing data, taking data, using AI algorithms, developing interfaces like human-machine interface (HMI), or machine-to-machine interfaces (M2M). (1-3) The smart grid is able to do these tasks: supporting application programming interface (API) management, security activities, maintenance activities, connecting the nodes in terms of cooperation, and supporting cloud service.