Self-adaptive deep learning optimization energy-saving control algorithm for central air-conditioning cold supply system

Abstract

The invention discloses a self-adaptive deep learning optimization energy-saving control algorithm for a central air-conditioning cold supply system. The algorithm comprises a power supply, a server data platform, a data connection port, a plurality of temperature sensors, a plurality of water flow sensors and a plurality of electric power sensors. According to the method, when the data volume is small, the physical model is utilized, global total energy consumption is used as a target function for optimization, a bad solution is eliminated and a new solution is regenerated in each iteration, and a high-dimensional optimization model is solved, so that the optimization calculation efficiency is effectively improved, and effective optimization control parameters are obtained more quickly and better; and a database is updated and a data model is trained by using real-time data, so that the problem that a physical model is not applicable under extreme working conditions is solved, the precision of an energy consumption model or a performance model of system power equipment is improved, and the optimization model is more accurate and effective. The network technology is adopted, cloud storage and remote transmission of data are achieved, the management efficiency of the cold supply system is greatly improved, and the system management cost is saved.

Description

technical field [0001] The invention relates to the field of air conditioning, in particular to an adaptive deep learning optimization energy-saving control algorithm for a central air conditioning cooling system. Background technique [0002] The cooling system has been widely used and paid attention to in the field of building air conditioning due to its significant effect on saving the total energy consumption of the building due to excessive energy consumption. The control of cooling system currently mainly adopts two control methods: (1) fixed control parameters, including chilled water supply temperature or return water temperature, chilled water pump frequency, cooling water pump frequency and cooling tower fan frequency; (2) based on expert strategies, More energy-saving control parameters are given by professionals according to the outdoor environmental conditions. The modeling method of cooling system mainly adopts two methods: physical model and data-driven mathe...

AI Technical Summary

Problems solved by technology

However, obtaining the energy consumption model through 3000 sets of data training and cross-validation requires a large number of parameters in actual situations, which is not conducive to application

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