## 5 Simulation Results
### Results of FIS with Humidity  
1 Energy consumption with proposed FLC in Hot Cities  
Two different simulation results for energy consumption in a day and for a month for comparison of techniques using and without using humidity as an input parameter are shown in Figures 19 and 20. In Figure 19, energy consumption for one day using Mamdani FIS and Sugeno FIS including and without including humidity is shown. Maximum hourly consumption for Mamdani FIS and Sugeno FIS without humidity is 5.7 kWh and 5.5 kWh, whereas maximum hourly energy consumption of proposed FLC using Mamdani FIS and Sugeno FIS with humidity is 4.5 kWh and 4.5 kWh. Our proposed FLC improves the energy consumption by effectively maintaining the user comfort up to 21% for both techniques. Figure 20 shows the monthly energy consumption of our designed FLC using both FIS with and without considering humidity. Energy consumption shown is calculated by evaluating and analyzing the fuzzy rule base. The energy consumption of FLC using Mamdani FIS without humidity is 2954 kWh, Sugeno FIS without humidity consumes 2837 kWh, Mamdani FIS with humidity shows 2270 kWh energy consumption and Sugeno FIS with humidity consumes 2295 kWh energy. Mamdani FIS with humidity improves energy consumption by 23% while Sugeno FIS with humidity is improving energy consumption by 22% as compared to the energy consumed using Mamdani FIS and Sugeno FIS without humidity. The proposed Mamdani FIS performs better than proposed Sugeno FIS because it is simple in nature and has more energy efficiency. As the demand for HVAC varies on an hourly basis in a residential building, the set-points of the thermostat are modified by using temperature and relative humidity information. These thermostat points have been initialized keeping in mind the relationship of temperature and relative humidity according to the psychometric graph so that user comfort is not jeopardized. Energy consumption with proposed FLC in Cold Cities  
As the proposed technique is the extension of [28], dealing with both hot and cold cities, this section discusses the energy consumption evaluation for the cold cities. Schedule of user occupancy and ToU rates remain the same for cold cities' evaluation. Outdoor temperature, indoor temperature, relative humidity, and thermostat set-point were used in accordance with cold city weather data. In Figure 21, energy consumption of the method proposed and the existing method for comparison is presented. The maximum energy consumption in cold cities is 6.26 kWh, 6.30 kWh, 4.5 kWh, and 4.5 kWh using FIS Mamdani FIS without humidity, Sugeno FIS without humidity, Mamdani FIS with humidity and Sugeno FIS with humidity. In a cold city scenario, Mamdani FIS and Sugeno FIS with humidity show 28% efficiency in energy consumption compared to FIS without humidity. Figure 22 shows the energy consumption of cold cities for one month simulation using Mamdani FIS and Sugeno FIS while considering and leaving the humidity parameter. Energy consumption is minimized while maintaining user comfort at a desired level using \(ISP_{s}\). Although the energy consumption for cold cities is greater as compared to hot cities, our proposed system succeeds in energy consumption minimization, which shows the efficiency of the proposed scheme compared to the earlier schemes. Monthly energy consumption of Mamdani FIS without humidity is 3629 kWh, Sugeno FIS without humidity is 3652 kWh, Mamdani FIS with humidity uses 2958 kWh and Sugeno FIS with humidity consumes 2922 kWh of energy. Efficiency in energy consumption for Mamdani FIS with humidity is 19%, whereas Sugeno FIS with humidity is 20% as compared to both FIS without humidity. Total Cost incurred with proposed FLC in Hot Cities  
Energy consumption minimization has the inevitable consequence of bill reduction. The cost for hot cities is calculated as using Equation (3). Figure 23 shows that proposed FLC performs better as compared to existing FLC for one day.