Multiple Criteria Decision Support for Heating Systems in Electric Transport

In this paper the solution to the problem of optimal control of climate parameters in public electric transport is proposed. Optimization of mechatronic system control is provided by minimization of electric energy consumption and maximization of passengers' comfort level. We propose to solve this task using artificial intelligence and progressive multiple criteria decision making methods. The popular Nelder-Mead multiple criteria decision making method (Nelder and Mead 1965) is applied. This method makes it possible to find a minimal value for the target function. In this case there is a dependence of minimal electric energy consumption on maximal comfort level. Our modelling and investigation is based on a typical architecture of heating ventilation and air conditioning system with a traditional application of AC induction motors for driving both a compressor and a fan of the conditioner. Special interest and further development is devoted to intelligent heating systems, allowing more flexible regulation of the system's compressor and fan operation, and, therefore, improvement of efficiency and energy saving.