## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.1 Energy Management Systems
(1-4) The cloud service is used in other services, such as taking care of numerous devices and big data, cloud data analytics, using dark data, developing an application, event management, being an interface or gateway between the hardware and software services. 2. steps of the proposed computing model, such as (2-1) designing devices, actuators, nodes, the cloud type, architecture of smart grid, and new services, (2-2) data flow that consists of several stages like capturing data by sensors, filtering data, transferring data between nodes and the smart grid, saving data, transferring data between nodes and management layer, transferring data to the cloud service, controlling data, and interfaces between human-machine and machine-machine. (2-3) Learning includes these stages: pattern identification models to identify the type of energy load, generation prediction to make a regression model, predictive maintenance to identify singular events, training other processes based on AI. (2-4) the operation comprises steps, all of which are related to processes, for one: supervision processes. (2-5) this step includes supervision, management, and maintenance. The proposed model is evaluated experimentally by means of a system that includes both wind and solar energy. The system is installed in a house, by which the effectiveness of the proposed method is proved. Based on the mentioned experiment, the advantages of the system are being easy to design, install, and operate, and being cost-efficient. All related services to the smart grid are suggested to be integrated into a developed API in the future. [PERSON], [PERSON], [PERSON] and [PERSON] [44] introduced a novel random neural network (RNN)-based model for smart control of heating ventilation and air conditioning (HVAC) by deploying IoT with WSN and cloud computing. The platform consists of wireless sensor nodes for measuring indoor environmental factors, HVAC duct sensor node--built with Arduino UNO board and MOTEINO R4 board for measuring environmental factors of inlet air from HVAC wireless base station, which is connected to the workstation through the serial port to generate the cloud processing scenarios,HVAC control panel, and web portal interface. The workstation on the server is used for data storage and training the RNN by using the hybrid particle swarm optimization and sequential quadratic programming (PSO-SQP) algorithm. Besides, it uploads the environmental factors/control signals on the cloud computing platform. Indoor and duct environment data are obtained by indoor sensors and HVAC duct sensors, respectively. The data is sent to the base station. The base station is connected to the workstation on the cloud for RNN analysis. The smart RNN controller calculates a number of occupants, estimates predicted mean vote (PMV)-based set points for cooling and heating, and learns from user preferences. An evaluation was done by testing an intelligent controller for HVAC in an environment chamber (\(12\times 8\times 8\) feet with one sensor node and one HVAC duct sensor) in the Glasgow Caledonian University campus. There is a chiller, which can reduce the air temperature to 5 \({}^{\circ}\)C. The base station is connected to the control panel of the chamber to control the heater, cooler, and ventilation on/off. The evaluation was carried out in three different cases. The results of occupancy estimation using RNN showed that the accuracy of occupancy is 92.5%. The power consumption of the intelligent controller in Case 3--embedding the intelligence in the base station and sensor nodes--is 4.4% lower than the other two cases. Case 3 performs better than other cases in terms of control decision delay. The control decision delay of case 3 is 7 ms.  
[PERSON] and [PERSON] [45] aimed to reduce the energy cost of appliances by utilizing renewable energy sources (RES) with respect to user's operation preferences by proposing a quality of experience (QoE)-aware SHEM system.