## 2 Related Work
### Fuzzy Inference System for DSM
In this study, AFLM has been integrated with the wireless sensor network to learn and adapt the thermostat set-point according to the user priorities. Learning vectors and adapting vector were used in order to learn the change in user preference and adapt according to the user's choice if the initialized set-points have been overridden by the user for consecutive three times. Simulations were performed for two months using \"Manual Mode\" and \"Autonomous Mode\" where \"Autonomous Mode\" was preferred by the user. In [28], a worldwide adaptive thermostat controller by implementing fuzzy logic has been presented. The proposed controller changes the thermostat set-points as the load demand changes. Input parameters to this system were user occupancy, utility price, outdoor temperature, and initialized set-point. This system was evaluated using Mamdani FIS and Sugeno FIS. Performance metrics were energy consumption, cost, PAR, and user comfort. Energy consumption was computed using FIS by performing MATLAB simulations where Sugeno FIS outperformed among all the approaches. The proposed technique resulted in low cost and avoidance of peak formation, but it also results in user discomfort. In [47], two demand-side management techniques i.e., load shifting and load curtailment, have been applied. In this research, the proposed techniques were Binary Particle Swarm Optimization Fuzzy Mamdani (BPSOFMAM) inference system and Binary Particle Swarm Optimization Fuzzy Sugeno (BPSOFSUG) inference system for controlling and scheduling electric loads. The proposed techniques were implemented on 10 single family apartments in order to control daily used appliances e.g., washer, dryer, etc. and seasonally used appliances e.g., air conditioner. BPSO was used to schedule daily used appliances during low peak hours, whereas fuzzy logic was used for maintaining thermostat set-points to improve the energy utilization efficiency. Air conditioning system set-points were initialized according to the PMV indexing method. Although the proposed technique outperforms in energy consumption minimization as compared to the existing approaches, user comfort is sacrificed. In literature, a fuzzy controller that aims to use ventilation for passive cooling of the residential building has been proposed in [48]. Cooling demand was reduced by optimizing the proposed controller using a multi-objective evolutionary algorithm. Although the proposed methodology considered the thermal comfort, the major focus was mainly on the cooling load of HVAC. A brief summary of the most relevant papers is presented in Table 2 only for quick lookup. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Technique** & **The objective** & **A limitation** \\ \hline An efficient power scheduling scheme [30] & Hybrid of Knapsack Problem (K-WDO) & Minimization of the time and electricity cost. & Thermal comfort is ignored. \\ \hline Air-conditioning system for proactive power demand response [32] & DSB and DFR response [32] & Cost and energy saving. & Use of synthetic dynamic prices and the system only works for officies. \\ \hline The smart thermostat using occupancy sensors [37] & Hidden Markov Model for occupancy based scheduling & Improved energy conservation. & Thermal comfort is sacrificed. Simulations are limited to only one type of HVAC. \\ \hline Occupancy behavior-based model predictive control [39] & Occupancy based Model Predictive Control (MPC) & User comfort enhancement and energy consumption minimization. & High computational cost and increase the complexity of the system. \\ \hline Hybrid Bacterial F