Heat exchange station predictive control method based on effective room temperature detection

Abstract

The invention discloses a heat exchange station predictive control method based on effective room temperature detection, and relates to the technical field of heat exchange station heating control. A heating stage is divided into an initial heating period, a stable heating period and a final heating period, and effective room temperature feedback control is adopted in the initial heating period and the final heating period. In the stable heating period, heating adjustment parameters calculated according to outdoor meteorological parameters are combined with effective room temperature feedback control for operation, operation data in the stable heating period serve as basic data of neural network training to train a neural network, and the trained neural network predicts the secondary network water supply temperature and the heat load in the next time period; and according to the characteristics of a heat exchange station in the whole heating period, a method of applying feedback control in the newly-built heat exchange station and the initial and final periods of heating operation and combining feedback control and predictive control in the stable heating period is adopted, so that the room temperature is controlled within a reasonable range in the whole heating period, the fluctuation of the heating room temperature is reduced, and on-demand heating is achieved.

Description

technical field [0001] The invention relates to the technical field of heat exchange station heating control, in particular to a heat exchange station predictive control method based on effective room temperature detection. Background technique [0002] my country's heating secondary network is mainly based on centralized regulation. The traditional control method is to manually adjust according to the heating curve. The adjustment process is based on experience. Thermal comfort quality is poor. The rise of heating predictive control based on machine learning provides an effective technical means for improving heating quality and reducing energy consumption. However, predictive control based on machine learning requires big data support. Under the conditions of new heat exchange stations and changes in the heating system, high outdoor temperature at the beginning and end of heating, and unstable heat demand, the predicted outdoor meteorological parameters will not be stable....

AI Technical Summary

Problems solved by technology

However, predictive control based on machine learning requires big data support. Under the conditions of new heat exchange stations and changes in the heating system, high outdoor temperature at the beginning and end of heating, and unstable heat demand, the predicted outdoor meteorological parameters will not be stable. It must be able to accurately respond to real-time working conditions, with strong randomness, and cannot provide accurate predictive control under the above circumstances

Therefore, in the absence of data support and large changes in working conditions, it is difficult to achieve on-demand heating of the system only by predictive control

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