## 2 Related Work
### Nature-Based Algorithms for DSM  
In the literature, it is observed that nature-based algorithms are often used for scheduling the appliances [29] as nature has provided a beautiful source to researchers for solving complicated problems from different research and problem domains. A mathematical Wind Driven Optimization (WDO) [30] technique has been used to achieve user comfort in terms of appliance waiting time and objective of electricity cost minimization. Appliances were divided into three different classes and the hybrid of WDO along with the Knapsack Problem (K-WDO) has been used. Simulations were performed in comparison with Particle Swarm Optimization (PSO), where the proposed technique results in better cost reduction. In order to increase the user convenience in residential buildings, an approach of the home energy management based on neural network and Q-learning algorithm has been proposed [31]. Results have shown that the technique not only reduced the peak formation and resulted in energy conservation but also helped in reducing carbon footprints. A novel air conditioning system [32] has been developed for better demand response by shifting the load in order to balance the power of the smart grid. The proposed system used two demand response strategies named Demand-Side Bidding (DSB) strategy and Demand as Frequency-controlled Reserve (DFR) strategy. The objective of the study was cost and energy saving while considering a building in Hong Kong with the synthetic dynamic price for RTP. Simulations have \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables and Abbreviations** & **Description** & **Variables and Abbreviations** & **Description** \\ \hline FS & Fuzzy Inference System & HEMS & Home Energy Management System \\ HVAC & Heating, Verification and Air Conditioning & PAB & Peak-to-Average Ratio \\ PCT & Programmable Communating Thermostat & ToU & Time of Use \\ \hline \begin{tabular}{c} _Temperature_ \\ _Oce_ \\ _ISP_, \\ \end{tabular} & Indoor temperature fuzzy input parameter & \(Temperature_{\textit{WDO}}\) & Outdoor temperature fuzzy input parameter \\ \hline \
\begin{tabular}{c} _Oce_ \\ _ISP_, \\ \end{tabular} &
\begin{tabular}{c} Occupancy fuzzy input parameter \\ Thermostat set point fuzzy input parameter \\ \end{tabular} & \(Phase_{\textit{WDO}}\) & Electricity take fuzzy input parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nomenclature. been performed for the 40-story office building using EnergyPlus. The proposed technique was compared with the conventional air conditioning approach, which results in reduction in cost and energy use. In order to balance the occupant comfort and residential energy consumption, a technique based on ZigBee-based sensor nodes and a machine learning approach has been proposed in [33]. The impact of different dynamic prices like ToU and Critical Peak Pricing (CPP) has been detailed in a survey [34]. Here, 15 homes were taken for experiments where the presence of central air conditioning and Programmable Communicating Thermostat (PCT) have been ensured. Similarly, another survey was performed in order to check the use of thermostat in ambient temperature variations and quantitative interviews of users were conducted for the comfort evaluation in Finland [35]. Additionally, the effect of occupancy using occupancy-responsive thermostat was observed in [36]. When the building was vacant, an occupancy-responsive thermostat change the initialized set-point to the setback. The Hidden Markov Model has been used to estimate the user occupancy status in [37]. A smart thermostat was proposed for turning the HVAC ON and OFF by following the user occupancy and sleep pattern. Motion sensors and door sensors were used to collect the data about a user schedule as well as simulations were performed in EnergyPlus.