Water chilling unit combined operation optimal control method based on model prediction

A chiller and combination technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of restricting the energy-saving operation of refrigeration systems, lag in operation adjustment, and not considering energy-saving problems, and achieves good applicability and construction. Die simple effect

A chiller and combination technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of restricting the energy-saving operation of refrigeration systems, lag in operation adjustment, and not considering energy-saving problems, and achieves good applicability and construction. Die simple effect

CN111256294AActive Publication Date: 2020-06-09深圳市得益节能科技股份有限公司
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  • Water chilling unit combined operation optimal control method based on model prediction
  • Water chilling unit combined operation optimal control method based on model prediction
  • Water chilling unit combined operation optimal control method based on model prediction

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Embodiment Construction

[0053] The specific implementation manner of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0054] ginseng figure 1 As shown, a GRNN-based chiller energy efficiency model modeling method proposed by the present invention can be implemented in the following steps:

[0055] S1: Establish a training data set for the GRNN model.

[0056] ① Record the automatic monitoring data of the refrigeration system, the recording interval is 10 minutes, and the monitoring parameters include the operating power W of each chiller i , Chilled water supply temperature t gi , Chilled water return temperature t h , Cooling water inlet temperature ct hi .

[0057] ②According to the monitoring data and the rated cooling capacity of the unit, the load rate of the computer unit As shown in the following formula:

[0058]

[0059] Among them, C is the specific heat of water, m i , t gsi , t h It is the measured flow rate of the i-th...

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Abstract

The invention provides a GRNN-based water chilling unit energy efficiency model modeling method and a water chilling unit group-control method based on cold load prediction and a water chilling unit energy efficiency model, and belongs to the field of water chilling unit energy saving. The water chilling unit energy efficiency model is established by using a GRNN technology and actual operating data of a refrigeration system, so that the energy consumption of the water chilling unit under different operating conditions can be predicted quickly and accurately. The operating unit number and theload rate distribution of the water chilling unit are optimized in real time by using the cooling load prediction data and the water chilling unit energy efficiency model with a goal of minimum systemenergy consumption, so that the operation energy efficiency of the refrigeration system is effectively improved.

Description

technical field [0001] The invention belongs to the field of energy-saving control of refrigeration systems, and in particular relates to an optimal control method for combined operation of chillers based on model prediction. Background technique [0002] The chiller is the core component of the central air-conditioning system, and its operating energy consumption accounts for more than 40% of the total energy consumption of the central air-conditioning. For a system in which multiple chillers are jointly operated in a large building, the energy efficiency of the chiller is different under different load rates. A reasonable chiller group control method is adopted to keep the chiller running as efficiently as possible and maximize the operating efficiency of the refrigeration system. It is an important technical way to realize energy saving of centralized air conditioning system. [0003] The traditional chiller group control adopts the feedback control method, generally by ...

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Application Information

Patent Timeline
09 Jun 2020
Publication
CN111256294A
IPC
F24F11/47; G06N3/04; G06N3/08; G06Q10/04
CPC
F24F11/47; G06N3/04; G06N3/08; G06Q10/04
Inventors
侯国峰; 孙育英