Multi-apartment building indoor areas temperature regime optimization algorithm efficiency improvement

Abstract

Abstract The relevance of solving energy-saving problems using mathematical optimization methods is approved. An overview of Russian and foreign works on the topic of energy conservation and energy optimization is given. The need for solving the problems of optimizing the thermos-hydraulic regimes of buildings as part of the Smart City project is explained. The article presents the mathematical formulation of the problem of optimizing the temperature regime of rooms using adjustable devices, as well as two methods for solving the problem: the directed search method and the genetic algorithm. The above algorithms are described, including the mathematical apparatus used. The objective function is described as the standard deviation of the temperature of the heated rooms. A method for calculating air temperature in a heated room is presented by solving the heat balance equation, including a method for calculating incoming heat flows from a radiator and outgoing heat flows through building envelopes. Calculation formulas are given. The idea of improving these methods through preliminary measures, which will consist in installing additional sections of radiators in “cold” rooms, is considered. An algorithm for the automated issuing of recommendations for installing additional sections of radiators in the required rooms is described. The results of a numerical experiment for the considered optimization methods are presented. The calculations were carried out for a typical building under average design conditions of the city of Izhevsk, taking into account the actual condition of the building envelope, the heating system of the building, including heating appliances premises. A comparative analysis of the convergence of the iterative process of the directed search method and the genetic algorithm is carried out. In conclusion, it was concluded that the introduction of preliminary measures improves the convergence of the iterative process of the optimization problem.