CN103049625A - Forecast management method for air compressor operation - Google Patents
Forecast management method for air compressor operation Download PDFInfo
- Publication number
- CN103049625A CN103049625A CN2011103053825A CN201110305382A CN103049625A CN 103049625 A CN103049625 A CN 103049625A CN 2011103053825 A CN2011103053825 A CN 2011103053825A CN 201110305382 A CN201110305382 A CN 201110305382A CN 103049625 A CN103049625 A CN 103049625A
- Authority
- CN
- China
- Prior art keywords
- air compressor
- compressor machine
- data
- air
- prediction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The invention provides a forecast management method for air compressor operation. The forecast management method for the air compressor operation is applicable to the forecast management according to historical data of an air temperature and humidity sensor and air compressor operation data. The forecast management method for the air compressor operation comprises the steps of reading a measuring result of the temperature and humidity sensor; storing the measuring result into a data recorder at certain intervals; determining whether air supply forecast of the air compressor needs to be performed or not; if not, storing the measuring result into the data recorder at set intervals; otherwise entering the data recorder according to the user preferences to obtain the needed historical data; building a calculation model through a mathematics calculation module; forecasting the state of the air temperature and humidity in a future time; combining the air compressor operation data to build an air compressor calculation model and estimating the air supply of the air compressor in a future time. Accordingly the energy demand and the energy consumption cost in recent days can be known in advance by combining the compressed air demand data and the air compressor operation and energy consumption characteristic data.
Description
Technical field
The present invention relates to relevant a kind of prediction management method, refer in particular to a kind of historical measurement data that can utilize air themperature and humidity, analyze the humiture variation tendency, Air Temperature, humidity in predict future a period of time, infer the air demand of air compressor machine, in conjunction with the air compressor machine bookkeeping, can reach the air compressor machine operation prediction management method of plant maintenance, maintenance, Power Management.
Background technology
General in factory the utilization of air compressor machine quite frequent, mostly be used for inflating or being applied in daily life such as various industries such as electronics, chemical industry, food, weaving or other.Therefore, the mode of operation of air compressor machine uses impact great for the energy of factory.But the air compressor machine operational administrative was guarantee process stable in the past, as long as sufficient and stable pressurized air are provided, excessive pressurized air often is provided, namely start the multi-section air compressor machine in the same time, make air compressor machine usually be in the state of idle running or low load, cause the waste of the energy.Minority possesses the environmental consciousness personage, though can operate the operational situation of each air compressor machine according to factory's compressed air demand, does not consider the service performance of air compressor machine.And the air compressor machine service performance is subjected to intake air properties influence (with reference to Fig. 1), the air of equal in quality, and under different temperatures and moisture condition, the volume size can change.Represent respectively under the temperature, moisture condition of different air the family curve of same air compressor machine such as the A among Fig. 1, B, C.When identical compressed air outlet pressure P 2, different intake air state, the air demand of same air compressor machine is difference to some extent.If fail to learn in advance the variation tendency of air characteristics, namely may cause the operational administrative misalignment, fail to reach the equilibrium of supply and demand and energy-conservation purpose, this is that prior art is badly in need of breaking through part.The inventor considers that this point designs, finally invention can break through the habitual management method scope of tradition, also can know in advance within the next few days demand for energy and power consumption cost, buy required quantity of energy and be ranked in advance the m of e time in order to it, have energy-saving and emission-reduction and promote its range of application and convenient effect.
Summary of the invention
For solving above-mentioned prior art weak point, the object of the invention is to provide the air compressor machine operation prediction management of improveing on a kind of method method, to overcome the problems of the prior art.The method is utilized the historical measurement data of air themperature and humidity, analyzes the humiture variation tendency, Air Temperature, humidity in predict future a period of time, the air demand of supposition air compressor machine.In conjunction with prediction gained air requirement, carry out system's operational administrative, can reach the best equilibrium of supply and demand, just enough air capacities (Energy on Demand) are provided, can allow again online air compressor machine remain on the good full carrying of efficient performance and turn state, reduce simultaneously equipment loss, reach purpose energy-conservation and the reduction maintenance cost.
Problem to be solved by this invention is, because the air compressor machine operational administrative was guarantee process stable in the past, sufficient and stable pressurized air only are provided, often start the multi-section air compressor machine at one time, make air compressor machine usually be in the state of idle running or low load, cause the waste of the energy and excessive pressurized air is provided.Some have the environmental consciousness personage, although can operate the operational situation of each air compressor machine according to factory's compressed air demand, do not consider the service performance of air compressor machine, so that cause the operational administrative misalignment and fail to reach the equilibrium of supply and demand and energy-conservation purpose.
In order to achieve the above object, the invention provides a kind of air compressor machine operation prediction management method, it comprises:
Step 1 reads the Temperature Humidity Sensor measurement result;
Step 2 is stored to data recorder with measurement result at set intervals;
Step 3 judges whether to carry out air compressor machine air demand prediction, if not, carries out the prediction of air compressor machine air demand, then enters above-mentionedly at set intervals measurement result to be stored to data recorder;
If step 4 if above-mentioned, carry out the prediction of air compressor machine air demand, then enters data recorder according to user's setting and obtains required historical data;
Step 5, mathematics calculation module construction computation model;
Step 6, the aerial temperature and humidity state of predict future a period of time;
Step 7 is set up the air compressor machine computation model, and this step is reached in conjunction with the air compressor machine service data;
Step 8, the air demand of the following a period of time air compressor machine of estimation.
Optimum, its above-mentioned steps one of the present invention reads the Temperature Humidity Sensor measurement result, utilizes temperature-humidity sensor, measures air themperature, the humidity of this air compressor machine air intake.
Optimum, its above-mentioned steps four of the present invention, if if above-mentioned, carry out the prediction of air compressor machine air demand, then enter data recorder according to user's setting and obtain required historical data, the data bulk that it obtains, according to user's setting and data recorder memory size, it can be the previous day, a few days ago or previous month measurement data.
Optimum, its above-mentioned steps seven air compressor machine service datas of the present invention comprise Air Temperature, the humidity of the air compressor machine air intake of air compressor machine air demand and synchronization.
Further, this step 5 of the present invention is utilized the construction method of mathematics calculation module construction computation model, and its method step comprises:
Step 1, the input data;
Step 2 is confirmed data integrity; If above-mentioned input data are imperfect, then enter and fill up obliterated data, and be back to the affirmation data integrity;
Step 3 is if above-mentioned input data integrity then enters the selection predictive mode;
Step 4, this selection predictive mode selectivity include one the prediction next time point and one the prediction the next cycle (my god);
Step 5, the next time point of above-mentioned steps four predictions is set up self-regression model by least square method;
Step 6, the four next cycles of prediction of above-mentioned steps (my god) then set up self-regression model by difference and least square method;
Step 7 is exported respectively above-mentioned steps five, six result of calculation.
Optimum, its above-mentioned steps two of the present invention fill up obliterated data, selective use interpolation method or historical data are reached.
According to air compressor machine air demand result of calculation of the present invention, pressurized air demand data and air compressor machine running energy dissipation behavior data in conjunction with identical production scheduling, can know in advance within the next few days demand for energy and power consumption cost, buy required quantity of energy and be ranked in advance the m of e time in order to it, tool energy-saving and emission-reduction and promote its range of application and effect easily.
Description of drawings
Fig. 1 is the air compressor machine performance plot under the different intake air characteristics.
Fig. 2 is system layout of the present invention.
Fig. 3 is air compressor machine operation prediction management method flow diagram of the present invention.
Fig. 4 is the structure process flow diagram of mathematics calculation module construction computation model of the present invention.
Fig. 5 is that the present invention utilizes the mathematics calculation module among Fig. 3 to make up the enforcement illustration that computation model carries out the air themperature prediction.
Fig. 6 is the air demand result of variations figure that utilizes the following a period of time air compressor machine of estimation among Fig. 3.
Detailed description of main elements
100 air compressor machines, 1001 air intakes
1002 use sides, 101 temperature sensors
102 humidity sensors, 103 pressure gauges
107 gas receivers
1 Temperature Humidity Sensor measurement result
2 are stored to data recorder with measurement result at set intervals
Whether 3 need to carry out the prediction of air compressor machine air demand
4 enter data recorder according to user's setting obtains required historical data
5 mathematics calculation module construction computation model
51 input data, 52 specified data integralities
53 fill up obliterated data 54 selects predictive mode
The next time point of 541 predictions
542 set up self-regression model by least square method
543 next cycles of prediction (my god)
544 set up self-regression model by difference and least square method
55 output result of calculations, 56 actual measurement data
57 predicted data
The aerial temperature and humidity state of 6 predict future a period of times
7 set up air compressor machine computation model 70 air compressor machine service datas
The air demand of the following a period of time air compressor machine of 8 estimations
81 temperature prediction result of calculations, 82 humidity result of calculations
83 air compressor machine air demand results of prediction and calculation.
Embodiment
For the effect of further understanding inventive features of the present invention, content and advantage and reaching, show with the present invention by reference to the accompanying drawings, and be described in detail as follows with the expression-form of embodiment.In this statement, employed figure among the present invention, its purport only is signal and aid illustration book usefulness, rather than the true ratio after the invention process and precisely configuration.So the claim of the present invention on reality is implemented should not be limited to ratio and the configuration relation of institute's accompanying drawing.
Fig. 2 is system layout of the present invention.
The present invention is in order to realize this air compressor machine operation prediction management method, and this system is the embodiment of an optimum, and it comprises: temperature sensor 101, humidity sensor 102, at least one air compressor machine 100, a gas receiver 107, a pressure gauge 103.
Said temperature sensor 101 is located at the porch of air intake 1001, so that the temperature characterisitic of measuring air intake 1001 its air to be provided.
Above-mentioned humidity sensor 102 is located at the rear of temperature sensor 101, so that the humidity characteristic of measuring air intake 1001 its air to be provided.
Above-mentioned at least one air compressor machine 100 is located at the rear of humidity sensor 102, and these air compressor machine 100 quantity are three in the present embodiment, but therefore do not limit the present invention, also can be any number of units, all belong to protection scope of the present invention.
Above-mentioned gas receiver 107 is located at the rear of at least one air compressor machine 100, and this gas receiver 107 provides the pressurized air storage of air compressor machine 100.
An above-mentioned pressure gauge 103 is connected with gas receiver 107, to measure the outlet air pressure of air compressor machine 100.
Fig. 3, Fig. 4 are air compressor machine operation prediction management method flow diagram of the present invention.The structure process flow diagram of mathematics calculation module construction computation model of the present invention.Because the present invention is applicable to the prediction management according to air temperature sensor 101 and humidity sensor 102 historical datas and air compressor machine 100 service datas, and be provided with respectively temperature sensor 101 and the humidity sensor 102 that the humiture data are provided in air intake 1001 places, and at gas receiver 107 places, then be provided with the pressure gauge 103 that the outlet air pressure of measuring air compressor machine 100 is provided, it comprises the following steps:
Step 1 read Temperature Humidity Sensor measurement result 1, and this reads Temperature Humidity Sensor measurement result 1, and it utilizes temperature-humidity sensor 101,102, measures temperature, the humidity of this air compressor machine air intake 1001;
Step 2 is stored to measurement result data recorder 2 at set intervals, and should the time period according to user's requirements set, should can be set as per 15 minutes the time period in the present embodiment, but do not limit the present invention with this, can be set as the various time periods yet, all belong to protection scope of the present invention;
Step 3 judges whether to carry out air compressor machine air demand prediction 3, if not, carries out the prediction of air compressor machine air demand, then enters the above-mentioned data recorder 2 that at set intervals measurement result is stored to;
If step 4 is if above-mentioned, carry out the prediction of air compressor machine air demand.Enter data recorder according to user's setting and obtain required historical data 4.Set as for, user and to enter the quantity that data recorder is obtained required historical data 4, fully according to the demand of using and the memory size of data recorder, it can be the previous day, a few days ago or previous month DATA REASONING quantity.For example: obtain per 15 minutes temperature history data of first three day, prepare to carry out the prediction of following one day temperature variation.For example: obtain per 15 minutes humidity historical data of first three day, prepare to carry out the prediction of following one day humidity;
Step 5, mathematics calculation module construction computation model 5;
Step 6, the aerial temperature and humidity state 6 of predict future a period of time, for example: following one day per temperature variation of 15 minutes.For example: per humidity of 15 minutes changed in following one day, but did not limit the present invention with this, also can be any a period of time, all belonged to protection scope of the present invention;
Step 7, set up air compressor machine computation model 7, and this step is reached in conjunction with air compressor machine service data 70, namely this sets up the front air compressor machine service data 70 that air compressor machine computation model 7 utilizes aerial temperature and humidity state 6 and the air compressor machine of predict future a period of time, comprises Air Temperature, the humidity of the air compressor machine air intake 1001 of air compressor machine air demand and synchronization; The air demand 8 of the following a period of time air compressor machine of estimation, the air demand 8 of the following a period of time air compressor machine of this estimation, for example variation of following one day per 15 minutes air compressor machine 10 air demands.Again since one day in the middle of, according to the difference of time, air themperature and relative humidity can change to some extent, also so that air compressor machine 100 air demands change simultaneously.But in general, every day temperature, humidity the variation situation be periodic the variation.Therefore, at air intake 1001 places by temperature sensor 101, humidity sensor 102 measured results, be above-mentioned steps one read Temperature Humidity Sensor measurement result 1, according to temperature and relative humidity data a few days ago, to be above-mentioned steps four according to the user set enters data recording and obtains required historical data 4, again through behind the mathematics calculation module construction computation model 5 such as above-mentioned step 5, just can carry out temperature and the prediction of relative humidity on the same day, the aerial temperature and humidity state 6 of predict future a period of time of above-mentioned steps six namely, and estimate the air demand of air compressor machine 100, i.e. the air demand 8 of the following a period of time air compressor machine of the estimation of above-mentioned steps eight.
Further, mathematics calculation module construction computation model 5 its construction method steps of the step 5 of the above-mentioned air compressor machine operation of the present invention prediction management method also comprise:
Step 1, input data 51 are for example after the temperature of first three day, the humidity historical data;
Step 2 is confirmed data integrity 52, if above-mentioned input data are imperfect, then enter and fills up obliterated data 53, and be back to the step of confirming data integrity 52.Fill up obliterated data 53 as for this, selective use interpolation method or historical data are filled up the obliterated data 53 of filling up of the data of omission and are reached.Relevant this confirmed specified data integrality 52, and for example the data break time is set to 15 minutes, and 4 data * 24 hour * 3 days=288 per hour then must be arranged, and like this, data are just complete;
Step 3 is if above-mentioned input data integrity then enters and selects predictive mode 54;
Step 4, these selection predictive mode 54 selectivity include prediction next time point 541 and one a next cycle of prediction (my god) 543, but do not limit the present invention with this, also can be various predicted time patterns, all belong to protection scope of the present invention, and this next time point 541, for example following 15 minutes, and the next cycle (my god) 543, for example following one day;
Step 5, the next time point 541 of the prediction of above-mentioned steps four is set up self-regression model 542 by least square method.In regression model, estimated value and actual observed value are more approaching better again.The purpose of least square method is to seek one group of parametric solution, and this group parameter can make the actual observed value of estimated value and synchronization, and the quadratic sum of both differences is minimum.Just can calculate the estimated value that makes new advances by this group parameter and historical observation data, this group parametric solution can be tried to achieve by normal equation;
Step 6, the next cycle of the prediction of above-mentioned steps four (my god) 543 items set up self-regression model 544 by difference and least square method; Be the poor of any two data points as for difference, can be used as trend and the periodic rule of eliminating the data increasing or decreasing.Non-stable data-switching is become stable data, during the predictive mode of selection cycle, because data periodically do not wish to see, so eliminate it periodically with method of difference when setting up computation model; And in the regression model, estimated value and actual observed value are more approaching better, the purpose of least square method is to seek one group of parametric solution, this group parameter can make the actual observed value of estimated value and synchronization, the quadratic sum of both differences is minimum, just can calculate the estimated value that makes new advances by this group parameter and historical observation data, this group parametric solution can be tried to achieve by normal equation;
Step 7 is exported respectively above-mentioned steps five, six result of calculation 55.
Fig. 5 is the enforcement illustration that the present invention utilizes the mathematics calculation module construction computation model among Fig. 3 to carry out the air themperature prediction.Dotted line is the continuous two days actual measurement data 56 of intake air temperature of the air compressor system of certain factory among the figure, utilize the data of the continuous two days actual measurement data of this intake air temperature 56 first days, temperature variation by mathematics calculation module construction computation model 5 prediction second days, its result is predicted data 57, and the temperature actual measured results with continuous two days actual measurement data 56 second days of intake air temperature compares afterwards; The present invention sets according to the user and the memory size of data recorder, mathematics calculation module can use the previous day, a few days ago or one month data calculate.In like manner, relative air humidity also uses identical way to predict.
Figure 6 shows that the present invention utilizes the air demand result of variations figure of the following a period of time air compressor machine of estimation among Fig. 3, wherein lower curve is the temperature prediction result of calculation 81 of aerial temperature and humidity state 6 of predict future a period of time of mathematics calculation module construction computation model 5 among Fig. 6, and the curve of the top is the humidity result of calculation 82 of mathematics calculation module construction computation model 5, utilize the humidity result of calculation 82 of the temperature prediction result of calculation 81 of mathematics calculation module construction computation model and mathematics calculation module construction computation model 5 in conjunction with the Air Temperature that comprises air compressor machine 100 air demands and synchronization, the air compressor machine service data 70 of humidity and set up air compressor machine computation model 7, the air compressor machine air demand results of prediction and calculation 83 of the variation of the air demand 8 of the following a period of time air compressor machine of estimation.
According to above-mentioned design, the present invention can be with temperature, the humidity of prediction air, the air output of estimation air compressor machine 10, in addition in conjunction with the pressurized air demand data of identical production scheduling, the air compressor machine operational administrative just can provide suggestion for operation for the user in advance, give the user the abundant reaction time, be easy to the work of corrective maintenance and maintenance in the factory.Simultaneously, if enough air compressor machines 100 service datas are arranged, can also predict the demand for energy of recent a few days and the power consumption cost in the factory, buy required quantity of energy and be ranked in advance the m of e time in order to it, tool energy-saving and emission-reduction and promote its range of application and convenient effect meet and utilize on novelty, creativeness, the industry and breakthrough.
In sum, the present invention has broken through the difficulty of existing manufacturing technology and structure, and has really reached the improved effect of institute's wish, and neither technological means commonly used, the process of neither those skilled in the art expecting easily.In addition, the present invention also discloses before application or delivers, and its novelty that has, creativeness, the obvious application condition that has met domestic and international patent of invention are so propose patent application in accordance with the law.
Above-described embodiment only is used for illustrating technological thought of the present invention and characteristics, its purpose makes one of ordinary skill in the art can understand content of the present invention and is implementing accordingly, should not limit claim of the present invention with this, be all equivalent variations or improvement of doing according to disclosed spirit, must be encompassed in the claim of the present invention.
Claims (6)
1. an air compressor machine operates the prediction management method, and the method comprises:
Step 1 reads the Temperature Humidity Sensor measurement result;
Step 2 is stored to data recorder with measurement result at set intervals;
Step 3 judges whether to carry out air compressor machine air demand prediction, if not, carries out the prediction of air compressor machine air demand, then enters above-mentionedly at set intervals measurement result to be stored to data recorder;
If step 4 if above-mentioned, carry out the prediction of air compressor machine air demand, enters data recorder according to user's setting and obtains required historical data;
Step 5, mathematics calculation module construction computation model;
Step 6, the aerial temperature and humidity state of predict future a period of time;
Step 7 is set up the air compressor machine computation model, and this step is reached in conjunction with the air compressor machine service data;
Step 8, the air demand of the following a period of time air compressor machine of estimation.
2. air compressor machine according to claim 1 operation prediction management method, wherein, step 1 read the Temperature Humidity Sensor measurement result, be to utilize temperature-humidity sensor, measure temperature, the humidity of this air compressor machine air intake.
3. air compressor machine according to claim 1 operates the prediction management method, wherein, if step 4, carry out the prediction of air compressor machine air demand, enter data recorder according to user's setting and obtain required historical data, wherein, the data bulk that obtains, according to user's setting and data recorder memory size, can be the previous day, a few days ago or previous month measurement data.
4. air compressor machine according to claim 1 operation prediction management method, wherein, the air compressor machine service data of step 7 comprises Air Temperature, the humidity of the air compressor machine air intake of air compressor machine air demand and synchronization.
5. air compressor machine according to claim 1 operates the prediction management method, and wherein, the construction method that utilizes mathematics calculation module construction computation model of step 5 comprises:
Step 1, the input data;
Step 2 is confirmed data integrity, if above-mentioned input data are imperfect, then enter and fills up obliterated data, and be back to the affirmation data integrity;
Step 3 is if above-mentioned input data integrity then enters the selection predictive mode;
Step 4, this selection predictive mode selectivity comprise prediction next time point and one a next cycle of prediction (my god);
Step 5, the next time point of the prediction of above-mentioned steps four is set up self-regression model by least square method;
Step 6, the next cycle of the prediction of above-mentioned steps four (my god) then set up self-regression model by difference and least square method;
Step 7 is exported respectively above-mentioned steps five, six result of calculation.
6. air compressor machine according to claim 5 operation prediction management method, wherein, step 2 fill up obliterated data, selective use interpolation method or historical data are reached.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011103053825A CN103049625A (en) | 2011-10-11 | 2011-10-11 | Forecast management method for air compressor operation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011103053825A CN103049625A (en) | 2011-10-11 | 2011-10-11 | Forecast management method for air compressor operation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103049625A true CN103049625A (en) | 2013-04-17 |
Family
ID=48062262
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011103053825A Pending CN103049625A (en) | 2011-10-11 | 2011-10-11 | Forecast management method for air compressor operation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103049625A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105927525A (en) * | 2016-05-19 | 2016-09-07 | 无锡汇能达科技有限公司 | Energy-saving gas supply control method for workshop gas |
| CN108960487A (en) * | 2018-06-13 | 2018-12-07 | 北京天泽智云科技有限公司 | Air compressor machine group system energy consumption optimization method and device based on big data analysis |
| CN113325906A (en) * | 2021-06-10 | 2021-08-31 | 上海电气风电集团股份有限公司 | Humidity control method, system, equipment and medium for electrical components of wind turbine generator |
| US11126765B2 (en) * | 2019-01-02 | 2021-09-21 | Dalian University Of Technology | Method for optimal scheduling decision of air compressor group based on simulation technology |
| CN118128737A (en) * | 2024-05-07 | 2024-06-04 | 德耐尔能源装备有限公司 | Intelligent exhaust pressure control method and device for protecting unit |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101382601A (en) * | 2007-09-05 | 2009-03-11 | 通用电气公司 | Method and system for predicting gas turbine emissions utilizing meteorological data |
| CN101718270A (en) * | 2009-11-20 | 2010-06-02 | 上海应用技术学院 | Prediction and pressure regulation method for control system of air compressor |
| US20100212413A1 (en) * | 2007-08-01 | 2010-08-26 | Man Turbo Ag | Method for Determining Emission Values Of A Gas Turbine, And Apparatus For Carrying Out Said Method |
-
2011
- 2011-10-11 CN CN2011103053825A patent/CN103049625A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100212413A1 (en) * | 2007-08-01 | 2010-08-26 | Man Turbo Ag | Method for Determining Emission Values Of A Gas Turbine, And Apparatus For Carrying Out Said Method |
| CN101382601A (en) * | 2007-09-05 | 2009-03-11 | 通用电气公司 | Method and system for predicting gas turbine emissions utilizing meteorological data |
| CN101718270A (en) * | 2009-11-20 | 2010-06-02 | 上海应用技术学院 | Prediction and pressure regulation method for control system of air compressor |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105927525A (en) * | 2016-05-19 | 2016-09-07 | 无锡汇能达科技有限公司 | Energy-saving gas supply control method for workshop gas |
| CN105927525B (en) * | 2016-05-19 | 2017-12-29 | 无锡汇能达科技有限公司 | Energy-saving gas supply control method for workshop gas |
| CN108960487A (en) * | 2018-06-13 | 2018-12-07 | 北京天泽智云科技有限公司 | Air compressor machine group system energy consumption optimization method and device based on big data analysis |
| US11126765B2 (en) * | 2019-01-02 | 2021-09-21 | Dalian University Of Technology | Method for optimal scheduling decision of air compressor group based on simulation technology |
| CN113325906A (en) * | 2021-06-10 | 2021-08-31 | 上海电气风电集团股份有限公司 | Humidity control method, system, equipment and medium for electrical components of wind turbine generator |
| CN118128737A (en) * | 2024-05-07 | 2024-06-04 | 德耐尔能源装备有限公司 | Intelligent exhaust pressure control method and device for protecting unit |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102779223B (en) | The method of short-term electric load prediction and device | |
| CN103049625A (en) | Forecast management method for air compressor operation | |
| CN102855343B (en) | Capacity prediction device, load Forecasting Methodology and load predictor | |
| US20130151179A1 (en) | Automated monitoring for changes in energy consumption patterns | |
| CN102314548B (en) | Apparatus and method for energy management | |
| US8532836B2 (en) | Demand response load reduction estimation | |
| EP2682914A1 (en) | Energy management method and system thereof, and gui method | |
| CN102799205B (en) | Monitoring method and monitoring system thereof of temperature and humidity of warehouse | |
| US20120215464A1 (en) | Energy consumption monitor | |
| CN104864549A (en) | On-line operation energy efficiency monitoring and evaluation system and method for air conditioner | |
| CN115016339B (en) | Monitoring method, equipment and medium for outdoor power equipment | |
| CN107292766A (en) | Towards the power system peak regulation means economic evaluation method and system of wind electricity digestion | |
| CN105204449A (en) | Aluminum profile extrusion machine real-time energy consumption monitoring and energy consumption abnormality detection system | |
| CN102789447A (en) | Method for analyzing ice and climate relationship on basis of grey MLR (Multiple Linear Regression) | |
| US9691111B2 (en) | Systems, methods, and apparatus for determining energy savings | |
| CN103471729A (en) | Device temperature early warning method and application thereof | |
| CN101860623A (en) | Method and system for indicating service time of intelligent phone battery by sensing system context | |
| CN114444370B (en) | Method and device for predicting accumulated loss life of rechargeable battery by considering operation conditions, electronic equipment and readable storage medium | |
| CN107292415A (en) | A kind of Forecasting Methodology of intelligent meter rotation time | |
| CN107036238A (en) | Intelligent energy-saving control method for dynamically predicting external air and load | |
| CN115146977A (en) | Enterprise energy efficiency data management method and system based on Internet of things | |
| CN105205566A (en) | energy consumption prediction method and system | |
| GB2473596A (en) | Device performance monitoring | |
| CN103163864B (en) | Method for optimizing mechanical equipment state estimation | |
| EP2490087A1 (en) | Energy consumption monitor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20130417 |