Central air conditioner energy consumption forecasting method based on operation data

A technology of operating data and central air-conditioning, which is applied in forecasting, data processing applications, calculations, etc., can solve the problems of lack of energy consumption prediction methods for central air-conditioning, and achieve the effect of reducing redundancy, significant significance, and reducing redundancy

Inactive Publication Date: 2018-12-07
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0005] In order to solve the problem of the lack of a universal central air-conditioning energy consumption prediction method in th

Method used

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  • Central air conditioner energy consumption forecasting method based on operation data
  • Central air conditioner energy consumption forecasting method based on operation data
  • Central air conditioner energy consumption forecasting method based on operation data

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Embodiment Construction

[0026] Embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0027] see figure 1 , a method for predicting energy consumption of central air-conditioning based on operating data proposed by the present invention is realized through the following steps:

[0028] Step 1, collect the operation data of the central air-conditioning system.

[0029] In this embodiment, as shown in the following table (1), the collected operating data includes bypass valve opening, chilled water flow rate, load rate, chilled water outlet pressure, chilled water return pressure, chilled water outlet temperature, and cooling water temperature difference , Chilled water return temperature, chilled water temperature difference, cooling water return temperature, cooling water outlet temperature, cooling pump frequency, freezing pump frequency and total system power.

[0030]

[0031] Table (1) Operating data characteristic parameters of ...

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Abstract

The invention provides a central air conditioner energy consumption forecasting method based on operation data, and belongs to the field of central air conditioner modeling research. Aiming at the highly non-linear central air conditioner modeling problem which is difficult to solve through a traditional mechanism analysis method and a system identification method, central air conditioner energy consumption is forecasted through a method combining a Boruta feature selection method with a BP neural network. The operation data is subjected to energy consumption feature selection through the Boruta feature selection method at first, an energy consumption feature subset is obtained, the energy consumption feature dimension of the operation data is reduced, and then, the redundancy rate of theenergy consumption feature subset is decreased through a pearson correlation coefficient method; and finally, the energy consumption feature subset serves as input of a BP neural network energy consumption model, the central air conditioner system total power serves output of the BP neural network energy consumption model, and a training BP neural network energy consumption forecasting model is reliable and pervasive and has important significance to energy saving strategy implementation.

Description

technical field [0001] The invention belongs to the research field of central air-conditioning modeling, in particular to a method for predicting energy consumption of central air-conditioning based on operating data. Background technique [0002] In the field of central air-conditioning modeling research, previous research work often started from the establishment of a mathematical model of the energy efficiency of the central air-conditioning system, identified the data model parameters of each device based on the system operating data, and finally determined the optimal simulation control strategy for the central air-conditioning system. Here we will This method is called the conventional method. Since the central air-conditioning system is an interconnected and interrelated operation, its energy efficiency mathematical model and identification model have complex structures, and the model parameters are difficult to obtain, and the mechanism modeling of conventional metho...

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Application Information

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IPC IPC(8): F24F11/46G06Q10/04F24F140/20F24F140/50F24F140/60
Inventor 龙欢杨平郝晓红蒋丹
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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