## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.1 Energy Management Systems
The proposed system uses the following items: (1) A QoE-aware cost-saving appliance scheduling (Q-CSAS) algorithm to schedule and control loads based on each user's profile; (2) A QoE-aware renewable source power allocation (Q-RSPA) algorithm to change the appliances operation schedule in case of surplus renewable electricity; (3) A new classification for thermostatically high load controlling in the appliance modeling; (4) Micro wind turbines and smart grid for RES power supply solar panels; (5) K-mean algorithm in MATLAB software for data clustering; (6) the silhouette value for selecting the optimal number in each cluster for each data (annoyance vector of the user's answers Qzw in the test). The proposed system evaluation and operation are as following: The SHEM system imposed in this paper is tested in a real-time scenario considering 1000 homes with pseudo-randomly choosing user profiles. A prototype has been implemented to validate the presented approach. For system technical evaluation and user evaluation, two groups of users, comprising a group of elderly and disabled people, and a group of younger, who are technologically experienced, have participated in a simulated environment. Each of the survey's pages remained is dedicated to a specific appliance, such as dishwasher (DW), heating ventilation and air conditioning (HVAC), and others. Five preferred times are set up for switchable appliances ranging from \(-3\) to \(+3\) h margin with a half an hour step. For thermostatically controlled appliances, set temperature for energy saving varies within a range of \(-3\) \({}^{\circ}\)C to \(+3\) \({}^{\circ}\)C with a 0.5 \({}^{\circ}\)C step for HVAC and \(-18\) \({}^{\circ}\)C to \(+18\) \({}^{\circ}\)C with a \(3\) \({}^{\circ}\)C step for the water heater (WH). The questions are answered separately for working days (WD) and days off (DO). As a result, 12 different types of appliance/period in a week is created. A k-means algorithm is used to cluster each user's profile of appliances usage. The optimal number of clusters is four clusters consisting of profile 0, which shows the users who are not willing to change the starting time or set temperature; profile 1, the user who is willing to change the defaults at the entire time range; profile 2, the user who wants to postpone the start time; profile 3, the user who is willing to choose the nearest time to the preferred one. After accepting the time shifts at the end of the task, the user rates the annoyance perceived. A new vector of annoyance is provided for both appliance and user, and its silhouette value is computed according to all the clusters to reassign the well-matched profile. Next, the Q-CSAS algorithm receives the best profile with the highest number of users of the reference appliance model. Task scheduling pattern is in the proposed SHEM, which conducts with Q-CSAS and Q-RSPA algorithms, respectively. Q-CSAS algorithm schedules G2 and G3 appliances tasks in off-peak times by sending appliance power consumption and duration times to the central unit to be analyzed. For G3 appliances, the activation takes place when the temperature set corresponds to a higher level of the annoyance of 1 scale. Simultaneously, the algorithm takes the lowest electricity price and combines it with the preferred time and temperature to schedule the best starting time. Whenever the power consumption needed by G2 or G3 appliances is lower than the surplus energy generated from RES, the Q-RSPA algorithm starts. G3 appliances' use of RES power depends on their temperature value. The results of system evaluation showed that (1) The achieved values of the silhouette in the QoE process of providing each profile range from 0.725 to 0.930, which are close to 1. This means that every example is matched with its cluster (usage profile). (2) In profile creation of the user's process, the larger shift in start times results in the greater annoyance scale. (3) The cost-saving amount of QoE-aware SHEM without RES is 22% and 33% with RES.