## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.1 Energy Management Systems
Four hidden layers are used in the best configuration system (MLP) with 512 neurons on each layer and using a dropout on the hidden last layer by a 50% reduction of the neurons. A quicker intersection is produced when using dropouts. Meanwhile, the second-best MLP is also considered with three layers that are hidden and 512 neurons for each layer without using dropout. Findings of the experiment showed that (1) 64% of the determination R\({}^{2}\) coefficient, 59.84% RMSE, 27% MAE, and 27.09% MAPE in the testing set results are conducted. (2) The NSM is the best feature for predicting energy consumptions among the mentioned algorithms. (3) Using only weather information, the proposed MLP displays a better error rate of 61.75% RMSE, and an accuracy R\({}^{2}\) 61%, 28.52% MAE, and 28.34% MAPE with around 7 k epochs. (4) The best scenario is with the weather and temporal data. (5) The second best MLP is tested in the best scenario (weather and temporal data), and the results are 63% R\({}^{2}\), 60.03% RMSE, 27.45% MAE, and 26.85% MAPE. (6) The best MLP scenario results are 57% R\({}^{2}\), 65.63% RMSE, 28.29% MAE, and 25.51% MAPE. (7) The training and validation results in the best scenario are 20.059 RMSE, 0.962 R\({}^{2}\), 10.810 MAE, 13.254 MAPE, and 59.840 RMSE, 0.643 R\({}^{2}\), 27.283 MAE, 27.096 MAPE, respectively. (8) The best performance of about 2% is resulted from the best scenario without lights by repeating the experiment for three times. (9) Adding temporal data enhances the outputs up to 1% in each experiment. (10) MLP is more sensitive than Gradient Boosting Machine (GBM) to light features, which decreases the functioning by 6%. (11) The temporal information increases the performance of each system, while the light feature decreases the functioning. (12) In corresponding performance, a 10% decrease is noted compared to other classifiers. (13) Lastly, the weather data is sufficient for energy consumption predictions. The validation of the system proposed requires further experiments on various datasets in future studies. [PERSON], [PERSON] and [PERSON] [47] proposed a system for controlling residential appliances with the lowest price and consumer satisfaction. The proposed system comprises (1) supervisory controller and (2) secondary controller (central controller), including fuse, power, CPU core, wireless and online transceivers, date and time module, data storage, RS485 port, power measuring unit. The system is also comprised of (3) smart plugs consisting of fuse, network disorders removal circuit, cut-off relay, power and current measurement unit, transceiver, central processor, monitoring unit, date and time module, pushbuttons, power, isolation circuit, reconnection unit, temperature sensor, brute-force search algorithm, conservation voltage reduction, and connection platform. The proposed system works based on the following stages: Firstly, in peak times or high tariffs, a central controller stabilizes the voltage in an appropriate quantity and reduces the input voltage to the devices. Secondly, the central controller transfers the commands to the smart plugs by applying the brute-force search algorithm. Finally, the brute-force search algorithm is utilized to optimize the problem, in which all possible models of consumption that meet the problem constraints are considered with the processor of the central controller. This system is evaluated by implementing a pilot home as an experiment, and the results of the pilot study showed that (1) In the control system considering time-of-use (TOU) tariffs, the highest consumption is recorded in times when the least electricity prices have been announced. (2) With applying restrictions along with a 10% voltage reduction, the total power consumption is less than the case without any aforementioned conditions and more than the case with only considered restrictions, although the user satisfaction is less in the last case. (3) Customer satisfaction, along with the reduction in consumption and cost, has been obtained.