College cloud energy intelligent control method

Abstract

The invention relates to a college cloud energy intelligent control method. The college cloud energy intelligent control method utilizes an adaptive neural network control method to realize the intelligent interaction of the college energy production and energy consumption, and provides a new method for the energy consumption supply and demand optimization matching and regulation control mode. A college cloud energy intelligent control platform mainly utilizes the Internet of Things and the automatic control technology to perform energy consumption and the data collection of the source production and the data analysis, utilizes the energy data to analyze and realize the energy source supply mode that the energy source production and the energy source production are guided by the energy source consumption so as to achieve the closed-loop operation of the college energy source system.

Description

technical field [0001] The invention relates to a control method, in particular to an intelligent control method for cloud energy in universities. Background technique [0002] Compared with traditional energy production and consumption models, the campus cloud energy system is a distributed energy system, which is greatly affected by external climate, weather and other factors, so the production capacity of distributed energy production is difficult to control. If the balance between energy production and energy demand cannot be guaranteed, the operation of the campus energy system may be chaotic and even lead to system collapse. At present, most of the control methods for energy control systems are based on off-line optimization or static optimization. The reason is that the real-time and complex characteristics of the energy network make it difficult to establish a reasonable mathematical model, and the energy consumption link passively consumes energy, and the energy pro...

AI Technical Summary

Problems solved by technology

[0002] Compared with traditional energy production and consumption patterns, the campus cloud energy system is a distributed energy system, which is greatly affected by external climate, weather and other factors, so the production capacity of distributed energy production is difficult to control

If the balance between energy production and energy demand cannot be guaranteed, it may cause chaos in the operation of the campus energy system and even lead to system collapse.

At present, most of the control methods for energy control systems are based on off-line optimization or static optimization. The reason is that the real-time and complex characteristics of the energy network make it difficult to establish a reasonable mathematical model, and the energy consumption link passively consumes energy, and the energy production link Detachment, resulting in low energy system efficiency, energy waste and other phenomena

The continuous intelligence of the energy consumption end and the continuous decentralization of the energy production end have caused the instability of the energy system, and the static optimization control based on the model is no longer suitable for the current energy system.

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