CN112966446B - Method for predicting cooling water circulation energy consumption in central air-conditioning refrigeration system - Google Patents
Method for predicting cooling water circulation energy consumption in central air-conditioning refrigeration system Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting cooling water circulation energy consumption in a central air-conditioning refrigeration system, and relates to the technical field of cooling water circulation energy consumption prediction. Acquiring data of the rotating speeds of all cooling water pumps in the cooling water circulation under different working conditions, the rotating speeds of fans of all cooling towers, the return water temperature of the cooling water, the water supply temperature of the cooling water and the like to obtain a historical data set aiming at equipment of the cooling water circulation to be predicted; then, preprocessing historical data to obtain an energy consumption vector of cooling water circulation; inputting the cooling water circulation energy consumption characteristic vector into a support vector regression algorithm, and calculating to obtain a cooling water circulation energy consumption model; and inputting the parameters of the current period and the last period into a cooling water circulation energy consumption model, and calculating to obtain the predicted energy consumption of the cooling water circulation. By applying the method, the energy consumption of cooling water circulation can be predicted in advance and accurately.
Description
Technical Field
The invention relates to the technical field of cooling water circulation energy consumption prediction, in particular to a method for predicting cooling water circulation energy consumption in a central air-conditioning refrigeration system.
Background
Industrial refrigeration (central air conditioning refrigeration systems) has always taken a large proportion of the energy consumption of industrial production. With the rapid development of the industrialization process, in order to meet the continuously expanding production requirements, the industry needs the central air-conditioning refrigeration system to generate more refrigerating capacity to cool the equipment. Therefore, more electric energy will be consumed, and energy conservation and emission reduction become an important target of each enterprise.
In the existing central air-conditioning refrigeration system, cooling water circulation mainly comprises a cooling water pump, a cooling tower fan and the like. However, the energy consumption of cooling water systems is not a very efficient way of managing. Enterprises often make annual, seasonal or monthly energy consumption plan targets, and the targets not only take structural characteristics, operating conditions and past energy consumption level of a central air conditioning system into consideration, but also take the targets of energy consumption management improvement, technical innovation of equipment systems and other factors into consideration.
The energy consumption completion condition in the prior art is generally analyzed by counting the average energy consumption data accumulated in the time period within the time period specified by the energy consumption control target, and analyzing the difference between the energy consumption level and the energy consumption control target.
If the energy consumption of cooling water circulation can be predicted in advance, the energy consumption level of the central air-conditioning refrigeration system can be reduced to a certain extent, the cost is reduced, and the refrigeration efficiency of the refrigeration system is improved.
Disclosure of Invention
The invention aims to provide a method for predicting cooling water circulation energy consumption in a central air-conditioning refrigeration system, which realizes the advance prediction of the cooling water circulation energy consumption.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for predicting cooling water circulation energy consumption in a central air-conditioning refrigeration system is characterized by comprising the following steps:
s1, obtaining historical data of the cooling water circulation to be predicted:
s1-1, acquiring the pump speed PS of each cooling water pump in cooling water circulation, the fan rotating speed FS of each cooling tower fan, the return water temperature RT of cooling water, the water supply temperature ST of cooling water, the flow F of cooling water, the outdoor temperature OT and the total energy consumption E of cooling water circulation;
s1-2, collecting and summarizing the data acquired in the step S1-1 by using a data acquisition module according to the time interval T;
s1-3, the data storage module stores the data in the database by taking the time stamp as a main key;
s2, preprocessing the historical data by the data preprocessing module to obtain a cooling water circulation energy consumption characteristic vector v, preprocessing the cooling water circulation energy consumption characteristic vector v, and storing the preprocessed cooling water circulation energy consumption characteristic vector v in a database;
s3, inputting the cooling water circulation energy consumption characteristic vector v into an energy consumption model training module, and calculating to obtain a cooling water circulation energy consumption prediction model;
s4, obtaining the current pump speed PS ' of each cooling water pump of the cooling water circulation to be predicted, the fan rotating speed FS ' of each cooling tower fan, the cooling water return water temperature RT ', the cooling water supply temperature ST ', the cooling water flow F ' and the outdoor temperature OT ', preprocessing the data to obtain energy consumption prediction characteristic vector data v ', inputting the energy consumption prediction characteristic vector data v ' into a cooling water circulation energy consumption prediction model, and obtaining the total energy consumption E ' of the cooling water circulation at the next time interval.
Still further, in step S1, the time interval T is not less than 10 minutes.
A further technical solution is that the specific process of the step S2 is as follows,
s2-1, converting the total energy consumption of the cooling water circulation in the historical data into the total energy consumption in a time period by the formula
E′ t ＝E t -E t-T ，
S2-2, converting the historical data into a cooling water circulation energy consumption characteristic vector v by taking the time stamp as a main key:
v＝(E t ，PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein E is t Total energy consumption of cooling water circulation for the previous time interval of time t, PS 1，t Number 1 at time t of the cooling water pump, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Number n for time t, PS n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 at time t of the rotational speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T For coding T-T timeNumber n of the rotational speed of the cooling tower fan, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow for time T-T, OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s2-3, carrying out data normalization processing on the cooling water circulation energy consumption characteristic vector;
and S2-4, storing the cooling water circulation energy consumption characteristic vector in a database.
A further technical solution is that the specific process of the step S3 is as follows,
s3-1, reading all cooling water circulation energy consumption characteristic vectors v from a database;
s3-2, inputting the cooling water circulation energy consumption characteristic vector v into a support vector regression algorithm in an energy consumption model training module, wherein the algorithm regression model is as follows:
E t ＝f(PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein, E t Total energy consumption of cooling water circulation for the previous time interval of time t, PS 1，t Number 1 at time t of the cooling water pump, PS 1，t-T Number 1 for T-T time, PS 2，t Rotational speed of cooling water pump numbered 2 at time t, PS 2，t-T Number 2 for T-T time, PS n，t Number n for time t, PS n，t-T Cooling water numbered n for T-T timeRotational speed of the pump, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 for time t, speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T Number n for T-T time, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow rate at time t, F t-T Cooling water flow for time T-T, OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s3-3, storing the support vector regression algorithm result as a model file.
A further technical solution is that the specific process of the step S4 is as follows,
s4-1, acquiring the pump speed PS 'of each cooling water pump for cooling water circulation, the fan rotating speed FS' of each cooling tower fan, the cooling water return water temperature RT ', the cooling water supply temperature ST' and the cooling water flow F 'through a PLC (programmable logic controller), and measuring the outdoor temperature OT' through a thermometer;
s4-2, collecting and summarizing the data by using a data acquisition module according to the time interval T;
s4-3, preprocessing the real-time data to obtain energy consumption prediction feature vector data v':
v＝(E t ，PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein, PS 1，t Rotational speed of cooling water pump numbered 1 at time t, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 for T-T time, PS n，t Rotational speed of cooling water pump, PS, numbered n at time t n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 for time t, speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T Number n for T-T time, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow for time T-T, OT t Outdoor temperature at time t, OT t- T is the outdoor temperature at the time T-T, T is the time interval, and n is the number of devices in the cooling water circulation;
s4-4, inputting the energy consumption prediction characteristic vector data v' into the cooling water circulation energy consumption prediction model to obtain the total energy consumption E of the cooling water circulation at the next time interval t 。
The further technical scheme is that the data preprocessing module performs dimensionality reduction on the data through Principal Component Analysis (PCA).
Compared with the prior art, the invention has the beneficial effects that: after the prediction model is established, the energy consumption of each device of the cooling water circulation in different states is predicted in advance, so that the energy conservation, fault diagnosis and the like can be realized.
Drawings
Fig. 1 is a flow chart of a method for predicting cooling water circulation energy consumption in a central air-conditioning refrigeration system according to the present invention.
Fig. 2 is a predicted energy consumption curve and an actual energy consumption curve of cooling water circulation in the central air-conditioning refrigeration system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the method for predicting cooling water circulation energy consumption in a refrigeration system of a central air conditioner comprises the following specific implementation steps:
s1: firstly, data acquisition is carried out on cooling water circulation to be predicted, and the method specifically comprises the following steps:
s1-1, obtaining the pump speed PS of each cooling water pump of cooling water circulation, the fan rotating speed FS of each cooling tower fan, the cooling water return water temperature RT, the cooling water supply water temperature ST and the cooling water flow F through a PLC, measuring the outdoor temperature OT through a thermometer, and obtaining the total energy consumption E of the cooling water circulation through an ammeter;
s1-2, collecting and summarizing the data acquired in the S1-1 by using a data acquisition module according to a time interval T, wherein the T is 15 minutes;
s1-3, the data storage module stores the data in the database by taking the time stamp as a main key;
s2: the data preprocessing module preprocesses historical data to obtain a cooling water circulation energy consumption characteristic vector v, and the specific steps are as follows:
s2-1, converting the total energy consumption of the cooling water circulation in the historical data into the total energy consumption in a time period by the formula
E t ＝E t -E t-T ；
S2-2, converting the historical data into a cooling water circulation energy consumption characteristic vector v by taking the time stamp as a main key:
v＝(E t ，PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein E is t Total energy consumption of cooling water circulation for the time interval before t, PS 1，t Number 1 at time t of the cooling water pump, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Rotational speed of cooling water pump, PS, numbered n at time t n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 at time t of the rotational speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T Number n for T-T time, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow at time T-T, OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s2-3, carrying out data normalization processing on the cooling water circulation energy consumption characteristic vector, and carrying out dimensionality reduction on the data through principal component analysis, namely PCA;
s2-4, storing the cooling water circulation energy consumption characteristic vector in a database;
s3: inputting the cooling water circulation energy consumption characteristic vector into an energy consumption model training module, and calculating to obtain a cooling water circulation energy consumption model, wherein the specific steps are as follows:
s3-1, reading all cooling water circulation energy consumption characteristic vectors from a database;
s3-2, inputting the cooling water circulation energy consumption characteristic vector into a support vector regression algorithm in an energy consumption model training module, wherein the algorithm regression model is as follows:
E t ＝f(PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein E is t Total energy consumption of cooling water circulation for the time interval before t, PS 1，t Rotational speed of cooling water pump numbered 1 at time t, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Number n for time t, PS n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 at time t of the rotational speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T The rotational speed of the cooling tower fan, RT, is numbered n for the time T-T t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow rate at T-T moment，OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s3-3, storing the support vector regression algorithm result as a model file;
s4: the method comprises the following steps of obtaining data of current cooling water circulation, inputting the data into a cooling water circulation energy consumption prediction module to predict cooling water circulation energy consumption, and specifically comprises the following steps:
s4-1, acquiring the pump speed PS of each cooling water pump for cooling water circulation, the fan rotating speed FS of each cooling tower fan, the cooling water return water temperature RT, the cooling water supply water temperature ST and the cooling water flow F through a PLC (programmable logic controller), and measuring the outdoor temperature 0T through a thermometer;
s4-2, collecting and summarizing the data acquired in the step 4-1 by using a data acquisition module according to a time interval T;
s4-3, preprocessing the real-time data to obtain energy consumption prediction feature vector data v':
v′＝(E t ，PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein, PS 1，t Rotational speed of cooling water pump numbered 1 at time t, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Number n for time t, PS n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 for time t，FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T The rotational speed of the cooling tower fan, RT, is numbered n for the time T-T t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature of cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow at time T-T, OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s4-4, inputting the energy consumption prediction feature vector data v' into a cooling water circulation energy consumption prediction model to obtain the total energy consumption E of the cooling water circulation at the next time interval t 。
Fig. 2 shows a refrigeration system of a tire company, and the cooling water circulation of the system comprises 12 cooling tower fans and 3 cooling water pumps. According to the training and prediction curve obtained in the implementation process, the abscissa in the graph is the testing times, and the ordinate is the energy consumption value. As can be seen from the figure, the energy consumption curve predicted by the predictive model is highly coincident with the actual energy consumption curve in 100 tests performed.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.
Claims (3)
1. A method for predicting cooling water circulation energy consumption in a central air-conditioning refrigeration system is characterized by comprising the following steps:
s1, obtaining historical data of the cooling water circulation to be predicted:
s1-1, acquiring the pump speed PS of each cooling water pump in cooling water circulation, the fan rotating speed FS of each cooling tower fan, the return water temperature RT of cooling water, the water supply temperature ST of cooling water, the flow F of cooling water, the outdoor temperature OT and the total energy consumption E of cooling water circulation;
s1-2, collecting and summarizing the data acquired in the step S1-1 by using a data acquisition module according to a time interval T;
s1-3, the data storage module stores the data in the database by taking the time stamp as a main key;
s2, preprocessing the historical data by the data preprocessing module to obtain a cooling water circulation energy consumption characteristic vector v, preprocessing the cooling water circulation energy consumption characteristic vector v, and storing the preprocessed cooling water circulation energy consumption characteristic vector v in a database;
s2-1, converting the total energy consumption of the cooling water circulation in the historical data into the total energy consumption in a time period by the formula
E t ′＝E t -E t -T
S2-2, converting the historical data into a cooling water circulation energy consumption characteristic vector v by taking the time stamp as a main key:
v＝(E t ，PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，
FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein E is t Total energy consumption of cooling water circulation for the time interval before t, PS 1，t Number 1 at time t of the cooling water pump, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Number n for time t, PS n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 at time t of the rotational speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T Number n for T-T time, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature of cooling water at time T-T, F t Cooling water flow rate at time t, F t-T Cooling water flow at time T-T, OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s2-3, carrying out data normalization processing on the cooling water circulation energy consumption characteristic vector;
s2-4, storing the cooling water circulation energy consumption characteristic vector in a database;
s3, inputting the cooling water circulation energy consumption characteristic vector v into an energy consumption model training module, and calculating to obtain a cooling water circulation energy consumption prediction model;
s3-1, reading all cooling water circulation energy consumption characteristic vectors v from a database;
s3-2, inputting the cooling water circulation energy consumption characteristic vector v into a support vector regression algorithm in an energy consumption model training module, wherein the algorithm regression model is as follows:
E t ＝f(PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，
FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein, E t Total energy consumption of cooling water circulation for the time interval before t, PS 1，t Number 1 at time t of the cooling water pump, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Number n for time t, PS n，t-T Number n for T-T time, FS 1，t Number 1 for time t, speed of the cooling tower fan, FS 1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 at time t of the rotational speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T Number n for T-T time, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow at time T-T, OT t Outdoor temperature at time t, OT t-T The outdoor temperature at the time T-T, T is a time interval, and n is the number of devices in cooling water circulation;
s3-3, storing the support vector regression algorithm result as a model file;
s4, obtaining the current pump speed PS ' of each cooling water pump of the cooling water circulation to be predicted, the fan rotating speed FS ' of each cooling tower fan, the cooling water return water temperature RT ', the cooling water supply temperature ST ', the cooling water flow F ' and the outdoor temperature OT ', preprocessing the data to obtain energy consumption prediction characteristic vector data v ', inputting the energy consumption prediction characteristic vector data v ' into a cooling water circulation energy consumption prediction model, and obtaining the total energy consumption E ' of the cooling water circulation at the next time interval;
s4-1, acquiring the pump speed PS 'of each cooling water pump for cooling water circulation, the fan rotating speed FS' of each cooling tower fan, the cooling water return water temperature RT ', the cooling water supply temperature ST' and the cooling water flow F 'through a PLC (programmable logic controller), and measuring the outdoor temperature OT' through a thermometer;
s4-2, collecting and summarizing the data by using a data acquisition module according to the time interval T;
s4-3, preprocessing the real-time data to obtain energy consumption prediction feature vector data v':
v′＝(E t ，PS 1，t ，PS 2，t ，...PS n，t ，PS 1，t-T ，PS 2，t-T ，...PS n，t-T ，FS 1，t ，FS 2，t ，...FS n，t ，
FS 1，t-T ，FS 2，t-T ，...FS n，t-T ，RT t ，RT t-T ，ST t ，ST t-T ，F t ，F t-T ，OT t ，OT t-T )
wherein, PS 1，t Rotational speed of cooling water pump numbered 1 at time t, PS 1，t-T Number 1 for T-T time, PS 2，t Number 2 at time t of the cooling water pump, PS 2，t-T Number 2 Cooling Water Pump rotational speed, PS, at time T-T n，t Number n for time t, PS n，t-T Number n for T-T, speed of the cooling water pump, F S1，t Number 1 for time t, of the cooling tower fan, F S1，t-T Number 1 for T-T moment, speed of the cooling tower fan, FS 2，t Number 2 at time t of the rotational speed of the cooling tower fan, FS 2，t-T Number 2 for T-T moment, speed of the cooling tower fan, FS n，t Number n for time t, speed of the cooling tower fan, FS n，t-T Number n for T-T time, RT t Is the return water temperature of the cooling water at time t, RT t-T Cooling water return temperature at time T-T, ST t Supply water temperature for cooling water at time t, ST t-T Supply water temperature for cooling water at time T-T, F t Cooling water flow at time t, F t-T Cooling water flow at time T-T, OT t Outdoor temperature at time t, OT t-T Is the outdoor temperature at the time T-T, T is the time interval, n is the cooling water circulationThe number of devices in the ring;
s4-4, inputting the energy consumption prediction characteristic vector data v 'into the cooling water circulation energy consumption prediction model to obtain the total energy consumption E' of the cooling water circulation at the next time interval.
2. The method for predicting the energy consumption of the cooling water circulation in the refrigeration system of the central air conditioner as claimed in claim 1, wherein: step S1 indicates that the time interval T is not less than 10 minutes.
3. The method for predicting the energy consumption of the cooling water circulation in the refrigeration system of the central air conditioner as claimed in claim 1, wherein: and the data preprocessing module is used for reducing the dimension of the data through principal component analysis.
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