In this article, to reduce energy consumption and manage its consumption in smart residential buildings, considering the convenience of people, a set of rules for determining intelligent temperature has been selected. For this purpose, expert rules and questionnaires have been prepared and used to make the indoor temperature intelligent based on individuals' emotional components, including clothing, outdoor temperature, age, body mass index, humidity, and the number of inhabitants. For this purpose, the ideal temperature under normal conditions of 22 degrees Celsius is considered by existing standards. The standard for determining the thermal indexes of PMV4 and PPD5 is used to validate the rules, and the result is acceptable compliance of these rules with the existing standard. According to the intervals set for the characteristics used, 1215 rules are defined for this system. A dashboard has been prepared in Excel software to adjust the temperature according to the existing rules, which is displayed as output by entering each available data based on qualitative and quantitative amounts of appropriate temperature. To evaluate the energy consumption, the two modes of temperature regulation with intelligent systems and manual temperature regulation have been compared. Results. For example, manually adjusting the temperature in 12 to 18 hours is a constant consumption pattern. By adjusting the temperature of the expert system per second, the consumption pattern changes based on residents’ satisfy. Manuscript profile

In this study, artificial neural networks, artificial neural network combination with genetic algorithm and neural network combination with Kalman filter were used to optimally model and control a real air conditioning system. Using the above methods, the system is first trained and after verifying the modeling accuracy, the capability of this modeling to predict the future conditions of the system is investigated. In addition to the subsystems investigated in both heating and cooling phases by mass and energy equations in Simulink simulated by Matlab software, the results of this section are finally compared with the optimal modeling results. The most important advantage of artificial neural network modeling over mass and energy equation modeling approaches is that it captures all the uncertainties and nonlinear properties of the air conditioning system due to the use of real data for modeling. It takes. Therefore, this method can optimize energy consumption in air conditioners by predicting the future conditions of the system and by precisely adjusting the time of turning on and off the main energy consuming equipment. The most important achievement of this research is more accurate and realistic modeling of the nonlinear air conditioning system.Comparing the methods used in the research for simulation methods using mass and energy equations, modeling using Bayesian trained neural network, artificial neural network modeling using MLP, modeling using neural network and genetic algorithm, modeling Using neural network and Kalman filter, the square error is equal to 0.006, 0.18, 0.056, 0.1456 and more than 0.5, respectively. Manuscript profile