## 2 Related Work
### Fuzzy Inference System for DSM  
A control strategy was developed for HVAC in order to respond to the RTP. A Dynamic Demand Response Controller (DDRC) [42] has been proposed that evaluates the value of electricity price at 15 min interval and change the thermostat set-point according to the setback initialized by the user. DDRC was implemented in MATLAB and the house model was integrated using EnergyPlus. A limitation of the proposed methodology is the use of a narrow range for temperature maintenance as they have not considered user preference. In [43], a fuzzy logic rule-based algorithm was developed along with wireless sensors' integration. A wireless programmable thermostat has been simulated in MATLAB. The proposed system used outdoor temperature, load demand, electricity price and user occupancy parameters in real time to reduce the thermostat set-points for better energy utilization in heating/cooling systems. Performance metrics are demand response participation, energy consumption, and the occupants' user comfort. The Center of Gravity (CoG) method is used for defuzzification, whereas membership functions of parameters are defined as a triangular shape. As the output parameter decides how much load reduction should be done, the defuzzified value tells by how much value an initialized set-point will change. The objective of this research was to reduce energy consumption while having a best indoor temperature, but user comfort is sacrificed. In the literature, it is found that a user often neglects or forgets to change the set-points according to varying price rates. In [44], an autonomous thermostat was developed by integrating fuzzy logic, wireless sensors, and smart grid initiatives. The proposed approach used Supervised Fuzzy Logic Learning (SFLL) including parameters of outdoor temperature, occupant presence, current electricity price and electricity demand to reduce the thermostat set-point. The proposed autonomous thermostat worked in two modes: (i) Economy Mode and (ii) Comfort Mode. The developed thermostat can handle all types of electricity pricing rates using Mamdani FIS. A limitation of the proposed system is its region-specific approach as research was conducted in a cold country--Canada. Because of advancement in technologies and communication, the Programmable Communicating Thermostat (PCT) is often recommended for use. PCTs automatically change the set-point of a thermostat according to the change in the pricing rate. PCTs are the thermostat that does not require the constant user interaction. Comparison between the conventional thermostat and programmable communication thermostat has been performed in [45] using computer simulation that was run for 24 h. In this paper, price-based demand response program of RTP for residential air conditioning load is used to reduce the peak electric load. The authors have considered three air conditioners with different power ratings to perform simulation in SIMULINK (R2013a, MathWorks, Natick, MA, USA). In [46], Fuzzy Logic Approach (FLA) is augmented with PCT to increase the working efficiency of thermostat for load reduction that can work for both RTP and ToU pricing mechanisms. Demand response has been implemented as proactive demand shedding using MATLAB GUI. A house simulator using thermodynamic principles was described in this paper. The input parameters were current outdoor temperature taken from sensors; electricity price communicated by the utility; occupancy from occupancy sensor and initialized set-points of thermostat, whereas the output of the system is load reduction. The proposed technique (PCT + FLA) has been compared with fixed thermostat, programmable thermostat, and PCT, showing that proposed work outperforms in energy consumption minimization. A limitation of this technique is its non-adaptiveness. The system proposed in [46] was made adaptive using Adaptive Fuzzy Logic Model (AFLM) in [17].