## 2 Related Works  
Metaheuristic and other intelligent algorithms are frequently chosen in solving the comfort index maximization and energy consumption minimization problem [28; 29]. The authors in [30] used GA to search for the best set of temperature, illumination, and air quality that offers the maximum comfort index and minimizes their difference with respect to the user's demand. The fuzzy controller is then used to reduce the power consumption in the building. Similarly, in [31], the authors optimized the user's comfort index using the three parameters. However, they applied the original ABC algorithm. Later, an enhanced ABC for the comfort index maximization named, ABC with a knowledge base is proposed in [32]. The knowledge base extension saved several optimal parameters from previous environmental conditions to help with the selection of the illumination, temperature, and air quality parameters of the next condition. In both [31; 32], a fuzzy controller is used for energy minimization. A hybrid FA-GA algorithm for comfort index maximization is proposed in [33; 34]. The GA operators are incorporated to address the drawbacks of the imbalanced exploration and exploitation in FA. The optimized parameters are also fed to fuzzy controllers for energy minimization. The performance of the hybrid algorithm is better than the original FA and GA. In [35], the authors proposed the application of BA for selecting the actuators' temperature, illumination, and air quality for the optimization of the comfort index and energy consumption of a building. On the other hand, deep extreme machine learning is used to predict the user's preference. The comfort index maximization and energy consumption minimization are approached as a single-objective optimization using a weighted technique in [36]. The user comfort gain and energy saving gain are combined into one objective function. Both PSO and GA are applied in this work for the optimization. The results show that PSO performs better for user comfort optimization, while GA is better in energy consumption minimization. Thermal comfort and indoor air quality are enhanced using an improved PSO and an adaptive network-based fuzzy inference system (ANFIS) in [37]. The improved PSO optimized the thermal comfort indicators layer-wise so that the best air conditioning parameters are obtained. The proposed system contributes to better thermal comfort and uniform temperature distribution within the room. In [38], non-dominated sorting GA-II (NSGA-II) is used for the minimization of energy used for providing thermal comfort through humidity, air speed, and temperature, as well as the percentage of people dissatisfied in the venue. The harmony enhanced differential evolution (HEDE), which is a hybrid of the enhanced differential evolution (EDE) and harmony search algorithm (HSA), is applied for a peak-to-average ratio reduction and appliances scheduling for the minimization of electricity costs in [39]. The home appliances' power consumption pattern is used to group them into three different classes, which are interruptible, non-interruptible, and base appliances. The classes are used to determine whether the appliances are interruptible and can be shifted to other time slots or not. A similar approach is taken in [40], where a cuckoo search (CS) algorithm is used for a multi-objective home energy management system (HEMS). The system schedules appliances based on their category, whether interruptible, non-interruptible, or non-schedule. Meanwhile, machine learning algorithms are used to forecast the home appliances' energy consumption in [41]. The forecasting allows the classification of the appliances by low- and high-power consumption. This categorization contributes to better energy management in the residential sector. From the four algorithms used in the work, logistic regression provides the highest accuracy. The K-nearest neighbor (KNN), multi-layer perceptron (MLP), and random forest (RF) are proposed in [42; 43] for the classification of high- and low-energy consumption residences.