CN112906306A - Prediction method for energy consumption of air compressor unit of air compression station - Google Patents
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Abstract
The invention discloses a prediction method for energy consumption of an air compressor unit of an air compressor station, and relates to the technical field of energy consumption prediction of the air compressor unit. Aiming at an air compressor unit to be predicted, acquiring data of capacity modulation, energy consumption of the air compressor unit, air pressure of an air storage tank and the like of each air compressor in the air compressor unit under different working conditions to obtain a historical data set; then preprocessing historical data to obtain an energy consumption vector of the air compressor unit; inputting the energy consumption characteristic vector of the air compressor unit into a support vector regression algorithm, and calculating to obtain an energy consumption model of the air compressor unit; adjusting the capacity of each air compressor at present by LDtLast period each air compressor capacity modulation LDt‑TAir pressure P of air storage tanktUpper period gas tank pressure Pt‑TInputting the energy consumption model of the air compressor unit, and calculating to obtain the predicted energy consumption E of the air compressor unitt(ii) a The air compressor unit to be predicted refers to an air compressor unit located at an air compression station of each industrial enterprise, and the types of the air compressors are different. By applying the invention, theThe energy consumption of the air compressor unit is predicted in advance and accurately.
Description
Technical Field
The invention relates to the technical field of energy consumption prediction of air compressor units, in particular to a prediction method of energy consumption of an air compressor unit of an air compression station in public works (public and auxiliary works).
Background
Compressed air is a standard configuration in various industrial enterprises, and is widely used in various occasions due to its excellent characteristics, and becomes the third largest energy source following fuels and electric power. The air compressor is used as a device for generating compressed air and is widely applied to various industries such as chemical industry, mine, metallurgy, electric power, textile, papermaking, plastic cement, tobacco, food, pharmacy and the like.
The air compressor used for generating compressed air is key power consumption equipment of industrial enterprises and has energy-saving potential. In the use cost of the air compressor, the equipment acquisition cost only accounts for 5%, the maintenance cost accounts for 18%, the electric charge accounts for astonishing 77%, and the use cost of the air compressor can be obviously reduced by implementing energy conservation of the air compressor.
The power consumption of the air compressor accounts for 15% of that of main industrial equipment, and the national air compressors consume about 2500 billion kilowatt-hour every year. According to the average electricity saving of 15 percent of air compressors in China and the average electricity price of 0.65 yuan, 244 million yuan of energy-saving benefit can be created every year, and the market prospect is wide. Industrial enterprises often make annual, seasonal or monthly power plan targets, which take into account not only the power demand, machine operating conditions, and past energy consumption levels of each production link, but also the targets of energy consumption management improvement, technical improvement of equipment systems, and other factors.
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 the air compression unit can be predicted in advance, the energy consumption level of the air compression station can be reduced to a certain extent, the cost is reduced, and the production efficiency is improved.
Disclosure of Invention
The invention aims to provide a method for predicting the energy consumption of an air compressor unit of an air compression station, which can be used for predicting the energy consumption of the air compressor unit in advance.
In order to solve the technical problems, the invention adopts the following technical scheme: a prediction method for energy consumption of an air compressor unit is characterized by comprising the following steps:
s1, acquiring historical data of the air compressor set to be predicted:
s1-1, obtaining capacity modulation values LD of air compressors in the air compressor unit, air pressure P of an air storage tank and total energy consumption E of the air compressor unit;
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, the data preprocessing module performs AND processing on the historical data to obtain an energy consumption characteristic vector v of the air compressor unit, and the energy consumption characteristic vector v of the air compressor unit is stored in a database after being preprocessed;
s3, training a model: inputting the energy consumption characteristic vector v of the air compressor unit into an energy consumption model training module, and calculating to obtain an energy consumption prediction model of the air compressor unit;
s4, verifying the model: and converting the current capacity modulation value LD ' of each air compressor of the air compressor unit and the pressure P ' of the air storage tank into energy consumption prediction characteristic vector data v ', and inputting the energy consumption prediction characteristic vector data v ' into an energy consumption prediction model of the air compressor unit to obtain the total energy consumption E ' of the air compressor unit at the next time interval.
A further technical solution is that the time interval T in step S1 is 5 minutes.
A further technical solution is that the specific process of step S2 is as follows:
s2-1, the data preprocessing module analyzes and eliminates singular values through variance detection;
s2-2, using 5/6 data in the preprocessed data as a training set, and using the rest 1/6 as a verification set;
s2-3, converting the historical data into an energy consumption characteristic vector v of the air compressor set by taking the time stamp as a main key:
v＝(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein, LD1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetFor the gas reservoir at time tAir pressure, Pt-TThe air pressure of the air storage tank is at the moment T-T, T is a time interval, and n is the number of the air compressors in the air compressor set.
A further technical solution is that the specific process of the step S3 is as follows,
s3-1, reading the energy consumption characteristic vector of the air compressor set of the training set from the database;
s3-2, inputting the energy consumption characteristic vector of the air compressor unit into a support vector regression algorithm in an energy consumption model training module, wherein the algorithm regression model is as follows:
Et＝f(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein E istTotal energy consumption, LD, of the air compressor set at the time interval before t1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank at the moment T-T, T is a time interval, and n is the number of air compressors in the air compressor set;
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, reading verification set data in the database;
s4-2, obtaining the energy consumption prediction feature vector data v' according to the following formula:
v＝(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein, LD1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TIs T-TCapacity modulation value, LD, of air compressor with scale number 12,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank is at the moment T-T, T is the time interval in the step 1-2, n is the number of air compressors in the air compressor set, and T is the current moment;
s4-3, inputting the energy consumption prediction characteristic vector data v' into an energy consumption prediction model of the air compressor unit to obtain the total energy consumption E of the air compressor unit at the next time intervalt’。
Compared with the prior art, the method can more accurately predict the energy consumption under various working conditions, and the average absolute percentage error (MAPE) of the method is as low as within 3 percent.
Drawings
Fig. 1 is a flowchart of a method for predicting energy consumption of an air compressor unit of an air compression station according to the present invention.
FIG. 2 shows the fitting effect of the energy consumption prediction model of the air compression station.
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
The scheme is the data of an air compression station of a certain steel plant in Shanxi province. The air compression station consists of five air compressors, five air storage tanks and one dryer. The air compressors and the air storage tanks correspond to each other one by one and then are connected to the dryer in parallel.
As shown in fig. 1, the method for predicting the energy consumption of the air compressor unit includes the following specific steps:
s1: the air compressor set to be predicted is firstly subjected to data acquisition for 5 weeks (about 1 ten thousand pieces of data are acquired in total), and then is subjected to preprocessing. The method comprises the following specific steps:
s1-1, obtaining a capacity regulating value LD of each air compressor of the air compressor unit through a PLC, measuring the pressure P of an air storage tank through a pressure sensor, and obtaining the total energy consumption E of the air compressor unit 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 5 minutes;
s1-3, the data storage module stores the data in the database by taking the time stamp as a main key;
s1-4, converting the total energy consumption of the air compressor set in the historical data into the total energy consumption in a time period, wherein the formula is as follows:
Et＝Et-Et-T；
s2, performing energy consumption prediction model modeling training by using the preprocessed data, which comprises the following specific steps:
s2-1, the data preprocessing module analyzes singular values through variance detection, eliminates the singular values, preprocesses the collected data to obtain about 9000 pieces of data, and stores the data in a database.
S2-2, 3000 pieces of data are randomly extracted from 9000 pieces of data in S1, 2500 pieces of data are randomly extracted as model training data (training set), and the remaining 500 pieces of data are used as verification data (verification set).
S2-3, converting the data into an energy consumption characteristic vector v of the air compressor set by taking the timestamp as a main key:
v＝(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein, LD1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank is T-T moment, T is a time interval, and n is that of the air compressor in the air compressor setThe number of the cells;
s3: inputting the energy consumption characteristic vector of the air compressor unit into an energy consumption model training module to an energy consumption model of the air compressor unit, and specifically comprising the following steps:
s3-1, reading the energy consumption characteristic vector of the air compressor set of the training set from the database;
s3-2, inputting the energy consumption characteristic vector of the air compressor unit into a support vector regression algorithm in an energy consumption model training module, wherein the algorithm regression model is as follows:
Et＝f(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein E istTotal energy consumption, LD, of the air compressor set at the time interval before t1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank at the moment T-T, T is a time interval, and n is the number of air compressors in the air compressor set;
s3-3, storing the support vector regression algorithm result as a model file;
s4: inputting the data of the verification set into an energy consumption prediction module of the air compressor unit for verification, and specifically comprising the following steps:
s4-1, reading verification set data in the database;
s4-2, obtaining the energy consumption prediction feature vector data v' according to the following formula:
v’＝(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein, LD1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TNull numbered 1 at time T-TVolume modulation value, LD, of a press2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank is at the moment T-T, T is the time interval in the step 1-2, n is the number of air compressors in the air compressor set, and T is the current moment;
s4-3, inputting the energy consumption prediction characteristic vector data v' into an energy consumption prediction model of the air compressor unit to obtain the total energy consumption E of the air compressor unit at the next time intervalt’。
S4-4, calculating the actual energy consumption E obtained in the S1-4tAnd energy consumption predicted by the model Et' A hyperbola is plotted for comparison, and the comparison results are shown in FIG. 2, with a calculated mean absolute percent error MAPE of 2.14%.
Although the invention has been described herein with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts or arrangements within the scope of the disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts or arrangements, other uses will also be apparent to those skilled in the art.
Claims (5)
1. A prediction method for energy consumption of an air compressor unit of an air compression station is characterized by comprising the following steps:
s1, acquiring historical data of the air compressor set to be predicted:
s1-1, obtaining capacity modulation values LD of air compressors in the air compressor unit, air pressure P of an air storage tank and total energy consumption E of the air compressor unit;
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, the data preprocessing module performs AND processing on the historical data to obtain an energy consumption characteristic vector v of the air compressor unit, and the energy consumption characteristic vector v of the air compressor unit is stored in a database after being preprocessed;
s3, training a model: inputting the energy consumption characteristic vector v of the air compressor unit into an energy consumption model training module, and calculating to obtain an energy consumption prediction model of the air compressor unit;
s4, verifying the model: and converting the current capacity modulation value LD ' of each air compressor of the air compressor unit and the pressure P ' of the air storage tank into energy consumption prediction characteristic vector data v ', and inputting the energy consumption prediction characteristic vector data v ' into an energy consumption prediction model of the air compressor unit to obtain the total energy consumption E ' of the air compressor unit at the next time interval.
2. The method for predicting the energy consumption of the air compressor unit of the air compressor station according to claim 1, wherein the method comprises the following steps: step S1 shows that the time interval T is 5 minutes.
3. The method for predicting the energy consumption of the air compressor unit of the air compressor station according to claim 1, wherein the method comprises the following steps: the specific process of step S2 is as follows,
s2-1, the data preprocessing module analyzes and eliminates singular values through variance detection;
s2-2, using 5/6 data in the preprocessed data as a training set, and using the rest 1/6 as a verification set;
s2-3, converting the historical data into an energy consumption characteristic vector v of the air compressor set by taking the time stamp as a main key:
v＝(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein, LD1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity of air compressor numbered 2 for T-T momentValue adjustment, LDn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank is at the moment T-T, T is a time interval, and n is the number of the air compressors in the air compressor set.
4. The method for predicting the energy consumption of the air compressor unit of the air compressor station according to claim 1, wherein the method comprises the following steps: the specific process of step S3 is as follows,
s3-1, reading the energy consumption characteristic vector of the air compressor set of the training set from the database;
s3-2, inputting the energy consumption characteristic vector of the air compressor unit into a support vector regression algorithm in an energy consumption model training module, wherein the algorithm regression model is as follows:
Et＝f(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein E istTotal energy consumption, LD, of the air compressor set at the time interval before t1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank at the moment T-T, T is a time interval, and n is the number of air compressors in the air compressor set;
s3-3, storing the support vector regression algorithm result as a model file.
5. The method for predicting the energy consumption of the air compressor unit of the air compressor station according to claim 1, wherein the method comprises the following steps: the specific process of step S4 is as follows,
s4-1, reading verification set data in the database;
s4-2, obtaining the energy consumption prediction feature vector data v' according to the following formula:
v＝(LD1,t,LD2,t,...LDn,t,LD1,t-T,LD2,t-T,...LDn,t-T,Pt,Pt-T)
wherein, LD1,tCapacity modulation value, LD, of an air compressor numbered 1 at time t1,t-TCapacity modulation value, LD, of air compressor numbered 1 at time T-T2,tCapacity modulation value, LD, of an air compressor numbered 2 at time t2,t-TCapacity modulation value, LD, of an air compressor numbered 2 at time T-Tn,tCapacity modulation value, LD, of an air compressor numbered n at time tn,t-TCapacity modulation value, P, of air compressor numbered n for T-T timetThe air pressure of the reservoir at time t, Pt-TThe air pressure of the air storage tank is at the moment T-T, T is the time interval in the step 1-2, n is the number of air compressors in the air compressor set, and T is the current moment;
s4-3, inputting the energy consumption prediction characteristic vector data v' into an energy consumption prediction model of the air compressor unit to obtain the total energy consumption E of the air compressor unit at the next time intervalt’。
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