## 6 Conclusions  
On the basis of the results, we have concluded that, by adding a feedback loop to the FIS with the input parameter of indoor temperature, outdoor temperature, occupancy, set-points, and price tariff, energy consumption required for a small room with a heater can be minimized as compared to previous techniques. Simulation results validated that the proposed method can reduce the energy consumption up to 50% in the worst case scenario. There are several different parameters that can be considered to evaluate the user thermal comfort e.g., temperature, humidity, metabolic rate, etc. Different sensors are available that can be used to monitor environmental parameters like temperature, humidity, and occupancy. In order to enhance the user thermal comfort and minimize the energy consumption, a humidity parameter was integrated to the existing FIS. Rules in the FIS are designed on the combination of the input parameter's membership function. Increasing the parameter increases the number of rules to be defined in the fuzzy rule base. In order to tackle different kinds of scenarios, defining more rules is beneficial. A method based on combinatorics was also defined, which assigns consequent to the antecedents of the fuzzy rules based on human intuition. MATLAB simulations were performed for one month that provides the validity of the proposed technique to reduce the energy consumption up to 28%. The proposed technique also maintained the user comfort while achieving electricity cost reduction up to 24%. By comparing the results of two different and well-recognized fuzzy inference systems, it can be concluded that Mamdani FIS with humidity outperforms the existing techniques in the hot cities, whereas Sugeno FIS is more efficient in the cold city scenario when the humidity parameter is added. The main reason behind the difference in the performance of a proposed fuzzy inference system is the working nature of these systems. Comparative to the hot cities, cold city scenarios are more complex, which makes it more compatible with the complex nature of Sugeno FIS. In hot cities, Mamdani outperforms because of its simplest nature. With the use of intelligent sensors and smart appliances, the proposed system is also capable of being implemented in IoT operating systems. The FIS proposed in this paper requires less memory, low processing power and real-time working capabilities. The proposed technique of FIS is able to be integrated in the RIOT, which is an operating system of networked IoT, mainly focusing on the low power devices with memory constraints. Possible further improvement of the proposed research can be the inclusion of more input parameters to take into account the real scenarios in order to decide how much energy consumption is required. As the system proposed only deals with HVAC, possible extension to include more house appliances is also possible in order to save energy and maintain user comfort. Enhancing the proposed system to work autonomously, which takes the information of the temperature and humidity from the sensor and decides the thermostat set-points automatically without user interaction is desirable for future work. Another possible extension to this system is to make the system adaptive according to user schedule. Because of global warming and depletion of fossil fuels, renewable energy sources are finding their way to the energy management system. This work can be further expanded to consider the potential effects of the renewable energy sources. This system can also be expanded by applying the same fuzzy controller to a large number of houses and can be integrated into a micro grid. As the amount of data increases when dealing with a large number of homes, this system can be further integrated with cloud computing or fog computing. Big data analysis is used to improve the scalability of the smart grid component and can be added to the existing technique because of an increase in the system data when dealing with a large number of residential buildings.