## 5 Simulation Results
### Results of FIS with Feedback  
1. Scenario I: In Scenario I, outdoor temperature represented the coldest day that is below 0 \({}^{\circ}\)C. As the temperature is very low, it is likely to take more energy consumption to maintain the inside temperature according to the user desired thermostat set-point. Figure 9 represents the room temperature variation with respect to the initialized set-points, outdoor temperature, and the heater state. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\#Rule** & \(T_{in}\) & \(T_{out}\) & \(Rate\) & \(Occupant\) & \(ISP\) & \(Humidity\) & \(EC\) \\ \hline
1 & L & L & HP & A & L & L & VL \\
2 & L & M & OP & P & L & L & M \\
3 & L & H & MP & P & M & H & M \\
4 & M & H & OP & A & H & H & H \\
5 & M & L & MP & P & M & M & M \\
6 & H & M & OP & A & L & M & M \\
7 & H & H & OP & P & H & H & VH \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample of rules defined in the proposed Fuzzy Inference System rule base. Figure 9: Temperature variation and heater state (Scenario I). Figures 10 and 11 show the daily energy consumption graph that is the output of using Mamdani FIS and Sugeno FIS with and without using a feedback loop. Figure 10 shows the comparison of Mamdani FIS when using feedback loop and without a feedback loop. As the outdoor temperature is very low, the heater is ON at a very large number of instances. It will constitute more energy consumption as compared to the other scenarios considered here. According to the Figure 10, energy consumption in a day using Mamdani FIS with feedback is 10.41 kWh and without feedback is 20.61 kWh. As the proposed technique used two FIS, Figure 11 shows the energy consumption by using Sugeno FIS including feedback loop and without a feedback loop. Energy consumption using Sugeno FIS without a feedback loop is 20.38 kWh and FIS with the addition of the feedback loop consumes 10.30 kWh of energy. Figure 12 shows the comparison of the proposed technique with the existing technique to show the effect of adding a feedback loop. As the energy consumption is computed at every 5 min interval by firing the rules from the fuzzy rule base that corresponds to the real-time scenario, it can be seen that fuzzy rule base with a feedback loop is getting realistic data, on the basis of which it turns the heater ON and OFF and saves the energy consumption accordingly. Energy consumption cost in a month without adding a feedback loop and using Mamdani FIS and Sugeno FIS is $56.05 and $54.95, respectively. From the above discussion, it can be concluded that Sugeno FIS is clearly the best performer among other considered methods. 2 Scenario II:  
Outside temperature in this scenario depicted a sunny day where the outdoor temperature reached the desired thermostat temperature, in this case there was no need to turn ON the heater and the simulation results shown in Figure 14 depict the same behavior of the heater state in response to outdoor and indoor temperature. During the afternoon, outdoor temperature rises and the heater is kept in an OFF state in order to conserve the energy, as compared to the previous technique, where the heater is still ON and is utilizing the energy because the previous approach does not consider the variation in the room temperature. Figure 12: Monthly energy consumption (Scenario I). Figure 13: Monthly electricity cost incurred (Scenario I). In this scenario, the outdoor temperature rises during the afternoon up to the desired thermostat set-point. During this time, there is no need for the heater to be turned ON and thus zero energy is consumed during these hours. Figures 15 and 16 clearly validate the working of the proposed FIS as the energy is not being consumed during those hours and the energy is conserved. As compared to the existing technique of Mamdani FIS, which consumes 16.71 kWh in the day, Mamdani FIS with a feedback loop consumes 3.39 kWh.