## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.1 Energy Management Systems  
Table 4 shows a summary of the 'energy management' theme. Detailed descriptions of the relevant practices identified in the AISH literature are presented after the table. In this group, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [41] designed a system architecture for increasing the energy efficiency of both smart homes and smart commercial buildings. The proposed model components include the following items:  
Figure 9: The word cloud based on the frequency of exact words, and the frequency of words within Group 3 of the AISH database considering word stems. 1. The architecture part working is based on Z-wave technology and is called PLUG-N-HARVEST, which includes four layers: adaptive dynamic building envelope (ADBE) that is related to an insulated aluminum facade, which is flexible, and energy collection device. Interconnected elements ecosystem (IEE) that includes all the sensors and automation devices. Security and safety mechanisms (SSM) that include fault detection and controlling the data. EMS, including three sub-systems--intelligent management and control system (IMCS), optimal OEMS, and demand response flexibility forecasting and optimization (DRFFO). 2. The cooperative part, which is a building management system (BMS) that consists of five security-by-design protocols, such as XACML, KeyRock, DCapBAC, EEC, and HTTPS. The model works based on the following steps: Firstly, the data is gathered by sensors attached to the building and collect the real-time data, and local stations or online services, which report and predict the weather information. Secondly, the EMSs (such as IMCS, OEMS, and DFRRO) can have access to the gathered data via BMS. Thirdly, IMCS makes the best control decisions and transfers them to the building actuators (such as heating ventilation and air conditioning (HVAC), adaptable/dynamic building envelope (ADBE), renewable energy system, and energy storage) via the BMS. Three different real-world prototypes are tested in order to show the proposed architecture (PLUG-N-HARVEST) in three countries (Germany, Greece, and Spain). Implementing the prototypes shows that the'security-by-design Internet communications' (refers to the security part of the research) and 'building equipment and cloud management systems' (refers to the PLUG-N-HARVEST) work efficiently together. The 'energy consumption' and 'comfort conditions' are also upgraded by the use of cloud intelligent management, and this result is based on the prototype implemented in Spain. [PERSON], [PERSON], [PERSON] and [PERSON] [42] proposed a system that tends to redesign an EMS in order to decrease the electricity bills and minimize the uncertainty of the power, which is exchanging between the grid and the user. The limitation of this work is that the proposed system has not been widely tested in real smart homes in different contexts. It includes a network of wireless sensors, electrical appliances, a cloud-based data collector, AI, and a programmed algorithm that is dynamic. The artificial neural network (ANN), named nonlinear auto regressive network with exogenous inputs (NARX), is used in the forecasting stage, in order to get better results. The wireless sensors use message queue telemetry transport (MQTT), which is a light-weight messaging protocol. The system works based on the following steps: (1) Forecasting stage that forecasts the amount of required load, environmental variables, and amount of generated load for its following day, based on previous data. (2) The planning stage, in which the user's cash flow is optimized, and a group of load reference values are made in this stage. The results of this stage measure the best grid-exchanges power profile (GEPP) for the following day. (3) Online re-planning stage, with the purpose of reducing the difference between the real and sent GEPP. It measures the real set of load references for this aim.