Reduce Energy Consumption by Optimizing ‎Temperature and Enforcing Smart Rules in ‎Residential Buildings

Document Type : Original Article


1 Department of civil engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.

2 Department of industrial engineering collage of engineering, karaj branch, Islamic Azad University, Karaj. Iran.


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.


[1]     Zekić-Sušac M, Mitrovića S, Has A "Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities," International Journal of Information Management, 2020.
[2]     Harkouss F, Fardoun F, Biwole P-H “Multi-objective optimization methodology for net-zero energy buildings,” Journal of Building Engineering, 2018, 16 57–71.
[3]     Lorena Tuballa M, Lochinvar Abundo M, “A review of the development of Smart Grid technologies,” Renewable and Sustainable Energy Reviews, 2016 Volume 59, Pages 710-725.
[4]     Mogles N, Padget J, Gabe-Thomas E, Walker I, Lee J, “computational model for designing energy behavior change interventions,” User Model User-Adap Inter, 2018, 28, 1–34.
[5]     Mohammadi M, Noorollahi Y, Mohammadi B, Hosseinzadeh, M, Yousefi H, Torabzadeh Khorasani S, “Optimal management of energy hubs and smart energy hubs – A review,” Renewable and Sustainable Energy Reviews, 2018.
[6]     Tarish Haider H, Hang See O, Elmenreich W, “A review of residential demand response of smart grid,” Renewable and Sustainable Energy Reviews, 2016, 59 166–178.
[7]     Joo I-Y, Chol D-H, “Distributed Optimization Framework for Energy Management of Multiple Smart Homes With Distributed Energy Resources,” Digital Object Identifier, 2017, 10.1109/ACCESS.2017.2734911.
[8]     Standard “Determination of thermal comfortPMV and PPD indices and localthermal comfort criteria,” Institute of Standards and Industrial Research of Iran, 1st. Edition, 1991.