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Balanced heat supply regulation and control method between secondary network households based on hybrid deep learning

A technology of balanced heating and deep learning, applied in neural learning methods, space heating and ventilation, space heating and ventilation details, etc., can solve problems such as high room temperature, increased system energy consumption, and rising heating parameters. High prediction accuracy, fast prediction efficiency, and the effect of reducing user complaint rate

Pending Publication Date: 2022-07-29
CHANGZHOU ENGIPOWER TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practice, some typical households may refuse to install the room temperature collection device for various reasons, and may also stop using the room temperature collection device for various reasons during use
[0005] To deal with complaints from heat users caused by the thermal imbalance of the secondary network, the heating company can only increase the heating operation parameters of the secondary network of the entire thermal station in order to increase the indoor temperature of the end residents during the dispatching process, so that the originally cold The user's room temperature is close to the standard, but due to the lack of restrictive measures for the overheated users, the heating parameters also increase synchronously, and the room temperature becomes higher
Because the indoor temperature is too high, many front-end users often adopt windows to ventilate to cool down, resulting in a lot of waste of heat energy, serious overheating of the entire thermal station, and increased system energy consumption

Method used

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  • Balanced heat supply regulation and control method between secondary network households based on hybrid deep learning
  • Balanced heat supply regulation and control method between secondary network households based on hybrid deep learning
  • Balanced heat supply regulation and control method between secondary network households based on hybrid deep learning

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Embodiment 1

[0087] figure 1 It is a flow chart of a method for controlling balanced heat supply between secondary network users based on hybrid deep learning involved in the present invention.

[0088] figure 2 It is a schematic diagram of the CNN-GRU network structure involved in the present invention.

[0089] image 3 It is a schematic diagram of the structure of the BiLSTM model involved in the present invention.

[0090] like Figure 1-3 As shown, this embodiment 1 provides a hybrid deep learning-based balanced heating control method between secondary network users, which includes:

[0091] Step S1, using mechanism modeling and data identification methods to establish a digital twin model of the secondary network of the heating system;

[0092] Step S2, transforming the hardware equipment of the Internet of Things for heat users, at least including: installing a heat meter at the entrance of the front heating pipeline of the typical heat user of the selected unit building, sett...

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Abstract

The invention discloses a method for regulating and controlling balanced heat supply among secondary network households based on hybrid deep learning. The method comprises the following steps: establishing a digital twinborn model of a secondary network of a heat supply system; the method comprises the steps that heat meters are installed at inlets of heat supply pipelines in front of households of typical heat consumers of selected apartments, data concentrators are arranged in all the buildings, and electric adjusting valves are installed at inlet branch pipes of heat consumer pipeline household heat supply systems; after correlation analysis of the multivariate data sequence and the room temperature of the user is established, room temperature data of the user of the building is obtained; based on the secondary network digital twinborn model, establishing a time-phased user demand load prediction model by adopting a first hybrid deep learning method, and obtaining a time-phased user demand load prediction value; and establishing a user valve control model by adopting a second mixed deep learning method in order to meet the room temperature requirement of the user, calculating the action state of an electric control valve in front of each user, and guiding the valve to control.

Description

technical field [0001] The invention belongs to the technical field of smart heating, and in particular relates to a balanced heating regulation method between secondary network users based on hybrid deep learning. Background technique [0002] As an important livelihood project, urban central heating has always attracted the attention of governments at all levels and the society. It is a key industry supported by the state in the field of infrastructure construction. Improving the quality of heating, reducing heating costs, and reducing pollution emissions have always been heating important subject of industry research. For a long time, because the hydraulic balance of the primary heating network involves the safe operation of the entire heating network, most heating companies attach great importance to it and invest a lot of money and energy in research and rectification. Significant results have been achieved, with significant reductions in the heat loss rate and water l...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F24D19/10G06N3/00G06N3/04G06N3/08G06Q10/04
CPCF24D19/1015G06Q10/04G06N3/08G06N3/006G06N3/044G06N3/045
Inventor 穆佩红裘天阅赵琼谢金芳
Owner CHANGZHOU ENGIPOWER TECH
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