CN103514338A - Method for predicting flow amount of blast furnace gas used by hot blast stove - Google Patents
Method for predicting flow amount of blast furnace gas used by hot blast stove Download PDFInfo
- Publication number
- CN103514338A CN103514338A CN201210199449.6A CN201210199449A CN103514338A CN 103514338 A CN103514338 A CN 103514338A CN 201210199449 A CN201210199449 A CN 201210199449A CN 103514338 A CN103514338 A CN 103514338A
- Authority
- CN
- China
- Prior art keywords
- blast furnace
- furnace gas
- heat generator
- gas use
- use traffic
- 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
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for predicting the flow amount of blast furnace gas used by a hot blast stove. First, main factors affecting changes of the flow amount of the blast furnace gas used by the hot blast stove are determined according to the production technology and the operation system of the hot blast stove, but the operation system of the hot blast stove is not considered according to a traditional prediction method, and thus a prediction model of the flow amount of the blast furnace gas used by the hot blast stove can respond to changes of operation of the hot blast stove better. Before the flow amount of the blast furnace gas used by the hot blast stove is predicted, only the gas flow mount and operation signals of the hot blast stove need to be predicted. Then, the prediction model of the flow mount of the blast furnace gas used by the hot blast stove is constructed with a modern regression modeling method, and the prediction model of the flow mount of the blast furnace gas used by the hot blast stove is faster in response and higher in prediction accuracy than a prediction model of the flow mount of the blast furnace gas used by the hot blast stove established with a traditional regression modeling method.
Description
Technical field
The present invention relates to areas of information technology, relate to heat generator production technology, operating duty and based on factor and data-driven modeling Regression Forecasting Technology, be specifically related to a kind of iron and steel enterprise heat generator blast furnace gas use traffic Forecasting Methodology.
Background technology
Iron and steel enterprise is major power consumer, and reasonable energy utilization is the target of iron and steel enterprise's effort all the time.For the by-product gas system of iron and steel enterprise, in order to achieve for balance, main dependence spot dispatch personnel allocate at present, realize the co-ordination of supply and marketing of coal gas.Heat generator is the main user of blast furnace gas, its blast furnace gas use traffic accounts for the whole production run blast furnace gas of iron and steel enterprise and uses the more than 30% of total flow, so the precision of prediction of heat generator blast furnace gas use traffic directly affects the effect that dispatcher regulates pipe network balance.
In prior art, adopted a kind of general Forecasting Methodology to predict heat generator blast furnace gas use traffic, this scheme has following problem: the influence factor of heat generator blast furnace gas use traffic is more, flow rate fluctuation is larger, and general modeling method is not high and be not very timely to the handoff response of stove operation to heat generator blast furnace gas use traffic precision of prediction.
Through retrieval, application number is 200710016562.5, this patent provides a kind of real-time control method for coal gas dynamic balancing in steel plants based on cabinet position prediction, this patent provides: a kind of real-time control method for coal gas dynamic balancing in steel plants based on cabinet position prediction: " the 1. real-time control method for coal gas dynamic balancing in steel plants based on cabinet position prediction, it is characterized in that, should at least comprise: a, data acquisition and supervisor control SCADA read in to SCADA real-time data base through communication drivers module by on-the-spot technological parameter by many groups front end measurement and control unit or gateway by on-the-spot coal gas system key parameter, each operating terminal issues in SCADA database by technological parameter data in network reading database and by the control command of each valve, SCADA database is sent to field control system through communication drivers module, realize the data acquisition of whole coal gas system technological parameter and the Long-distance Control of key equipment, b, utilize gasmeter cabinet position prediction module, take current gas balance amount data for the previous period as basis, the system identifying method based on least square method, the parameter of on-line identification system, and make a prediction, according to predicting the outcome, gas balance distribution module is carried out the adjustment balance of gas allocation amount, c, cabinet position prediction module and gas balance distribution module by interface module, realize and real-time data base between exchanges data, by affecting the stable real time data of gas holder position and pipe network, pass to cabinet position prediction module and receive the operation result from prediction module, being shown in coal gas SCADA, d, according to the position relationship of the trend prediction curve of gas holder position and predicted value and desired value, setting value, calculate the capable of regulating amount of coke-oven gas, blast furnace gas, e, the SCADA man-machine interface of adjusting by coal gas Real-time Balancing, by coal gas system technological parameter comprehensively, show intuitively." use traffic forecast model of the present invention is than the heat generator blast furnace gas use traffic forecast model predicated response speed that adopts traditional regression modeling method to set up is faster and precision of prediction is higher.
Summary of the invention
For above-mentioned defect of the prior art, the technical problem to be solved in the present invention is to provide a kind of iron and steel enterprise heat generator blast furnace gas use traffic Forecasting Methodology, Hot blast stove blast furnace gas use traffic variation tendency more exactly, the balance scheduling that completes coal gas for spot dispatch personnel provides reasonable guidance.
For realizing above-mentioned object, the technical solution used in the present invention is: first, employing determines according to heat generator production technology and operating duty the principal element that affects the variation of heat generator blast furnace gas use traffic, and Classical forecast method is not considered stove operation system, this makes the variation that can respond better stove operation of this heat generator blast furnace gas use traffic forecast model.Before the prediction of heat generator blast furnace gas use traffic, only need to predict gas flow and the stove operation signal of major influence factors.Then, utilize modern regression modeling method to build heat generator blast furnace gas use traffic forecast model, this heat generator blast furnace gas use traffic forecast model is than the heat generator blast furnace gas use traffic forecast model predicated response speed that adopts traditional regression modeling method to set up is faster and precision of prediction is higher.
Further, heat generator blast furnace gas use traffic Forecasting Methodology of the present invention, specifically comprises the steps:
Step 2, determine heat generator working system, for example " two burn one send " or " three burn one send ", " parallel connection " or " alter-parallel " etc.Select blast funnace hot blast stove to change stove signal as the influence factor of heat generator blast furnace gas use traffic.
Step 3, due to air-supply time, hot blast temperature and hot blast stove burning time exist substantial connection (along with air-supply the time prolongation, wind-warm syndrome reduces gradually), therefore, select wind pushing temperature and air flow rate to change stove signal as the influence factor of heat generator blast furnace gas use traffic as blast funnace hot blast stove.
The actual production data such as step 4, acquisition heat generator blast furnace gas use amount, each arm blast furnace gas use amount of heat generator, changing-over stove signal and blast furnace wind pushing temperature and air flow rate;
Step 5, actual production data are carried out to the data pre-service such as data polishing, filtering and normalization;
Step 7, based on seasonal effect in time series prediction thought, set up the time series predicting model of each influence factor;
Step 8, according to influence factor time series predicting model, the gas flow of influence factor or signal are predicted, and as the input item of heat generator blast furnace gas use traffic factor forecast model, heat generator blast furnace gas use traffic is predicted.
The adopt method of the present invention heat generator blast furnace gas use traffic variation tendency of predict future a period of time more accurately, make the variation tendency that dispatcher can reference thermal wind furnace blast furnace gas use traffic, in conjunction with existing scheduling experience, coal gas is carried out to rational management; Realize Fast-Balance gas using quantity, reduce coal gas diffusion, improve gas utilization rate.
The present invention has effectively reduced dispatcher's despatching work amount, and predict the outcome than the result of artificial estimation more in time, more accurate.
The present invention is before setting up heat generator blast furnace gas use traffic factor forecast model, first determine and affect the input of the principal element of heat generator blast furnace gas use traffic variation before as heat generator blast furnace gas use traffic factor forecast model, then adopt intelligent regression modeling method to set up heat generator blast furnace gas use traffic factor forecast model, reduce the complexity of model, improved the precision of prediction of model.
Owing to having adopted technique scheme, the invention has the beneficial effects as follows: the adopt method of the present invention heat generator blast furnace gas use traffic variation tendency of predict future a period of time more accurately, make the variation tendency that dispatcher can reference thermal wind furnace blast furnace gas use traffic, in conjunction with existing scheduling experience, coal gas is carried out to rational management; Realize Fast-Balance gas using quantity, reduce coal gas diffusion, improve gas utilization rate.
Accompanying drawing explanation
Fig. 1 is the heat generator blast furnace gas use traffic forecasting process process flow diagram of the embodiment of the present invention 1;
Fig. 2 is that the heat generator blast furnace gas use traffic of the embodiment of the present invention 1 changes and the comparison diagram that predicts the outcome.
Embodiment
Below in conjunction with drawings and Examples, the technical solution of the present invention is further explained, but following content is not intended to limit the scope of the invention.
Embodiment 1: the prediction of Baosteel 1# blast funnace hot blast stove blast furnace gas use traffic
Shown in Figure 1, in iron and steel enterprise described in embodiments of the invention 1, heat generator blast furnace gas use traffic Forecasting Methodology realizes in accordance with the following steps:
Step 2, selects No. 1 blast funnace hot blast stove to change stove signal as heat generator blast furnace gas use traffic forecast model influence factor.
Step 3, selects No. 1 blast furnace air flow rate and wind pushing temperature as heat generator blast furnace gas use traffic forecast model influence factor.
Step 4, reads and processes heat generator blast furnace gas use traffic correlative flow and signal data.By the real-time data base in on-the-spot energy resource system, read the required influence factor data on flows of Hot blast stove blast furnace gas use traffic, and to data carry out that dimension is unitized, normalization and noise reduction process.
Step 5, utilizes influence factor, structure training sample set S.
Wherein,
with
composition model input sample,
for model output sample,
represent P influence factor previous moment flow,
the heat generator blast furnace gas use traffic that represents previous moment,
the heat generator blast furnace gas use traffic that represents current time.
Step 7, utilizes existing production data, based on time series forecasting thought, adopts intelligent regression modeling method to set up each influence factor volume forecasting model.
Concrete grammar is:
A, the gas flow of influence factor and signal data are carried out to medium filtering and normalized;
B, utilize G-P algorithm to determine the embedding dimension m of each major influence factors;
C, utilize phase space to change to obtain training sample set S
i={ (x
j, y
j) | j=1,2 ..., n
i-m
i}:
(wherein, x
j∈ R
mirepresent the input of influence factor time series predicting model, y
j∈ R represents the output of model.）
D, the Time Series Forecasting Methods of employing based on intelligent algorithm are set up each influence factor forecast model.
Step 8, the heat generator blast furnace gas use traffic variation tendency of predict future in a period of time.Utilize each major influence factors forecast model to obtain gas flow and the signal estimation value of each major influence factors; This flow and signal estimation value are inputed to heat generator blast furnace gas use traffic factor forecast model, obtain each prediction heat generator blast furnace gas use traffic variation prediction trend (predicted value) of correspondence constantly.
In a case study on implementation of the present invention, heat generator blast furnace gas use traffic changes and predicts the outcome more as shown in Figure 2; Acquisition time is on June 20th, 2011, and cover time scope is 19:24～21:03, and corresponding data is in Table 1; Curve 1 is for adopting heat generator blast furnace gas use traffic actual value curve, and curve 2 is for adopting predicting the outcome of BP neural net method, and curve 3 is for adopting predicting the outcome of SVM method, and curve 4 predicts the outcome for the inventive method.The heat generator blast furnace gas use traffic variation tendency that adopts as seen from Figure 2 method prediction of the present invention to obtain approaches the actual change of heat generator blast furnace gas use traffic most.
The table 1 heat generator blast furnace gas use traffic comparison sheet that predicts the outcome
Forecasting Methodology of the present invention is based upon on the energy management system basis of existing each iron and steel enterprise,
The energy management system of Yi Ge iron and steel enterprise is as carrier.For spot dispatch personnel, easily grasp, can alleviate dispatcher's prediction work, dispatcher is placed on main energy in the formulation of reasonable gas dispatching strategy.
The present invention can make full use of heat generator production technology, operating duty and production history data, according to the influence factor that affects the variation of heat generator blast furnace gas use traffic, set up effective heat generator blast furnace gas use traffic forecast model and come the heat generator blast furnace gas use traffic of predict future in a period of time to change, thus guide field dispatcher scientifically and rationally the product of balance coal gas disappear; Can effectively overcome existing iron and steel enterprise artificial Hot blast stove blast furnace gas use traffic workload large, the problem that precision is lower.
By specific embodiment, the present invention is had been described in detail above, but these are not construed as limiting the invention.Without departing from the principles of the present invention, those skilled in the art also can make many distortion and improvement, and these also should be considered as protection scope of the present invention.
Claims (5)
1. a heat generator blast furnace gas use traffic Forecasting Methodology, is characterized in that specifically comprising the steps:
Step 1, determine that blast furnace is equipped with heat generator seat number, selects each heat generator arm flow of blast furnace as the influence factor of heat generator blast furnace gas use traffic;
Step 2, determine heat generator working system, select blast funnace hot blast stove to change stove signal as the influence factor of heat generator blast furnace gas use traffic;
Step 3, selection wind pushing temperature and air flow rate change stove signal as the influence factor of heat generator blast furnace gas use traffic as blast funnace hot blast stove;
Step 4, acquisition heat generator blast furnace gas use amount, each arm blast furnace gas use amount of heat generator, changing-over stove signal and blast furnace wind pushing temperature and air flow rate actual production data;
Step 5, actual production data are carried out to data polishing, filtering and normalization data pre-service;
Step 6, based on actual production data after pre-service, use intelligent regression algorithm to set up the heat generator blast furnace gas use traffic factor forecast model embodying between heat generator blast furnace gas use traffic and its influence factor;
Step 7, based on seasonal effect in time series prediction thought, set up the time series predicting model of each influence factor;
Step 8, according to influence factor time series predicting model, the gas flow of influence factor or signal are predicted, and as the input item of heat generator blast furnace gas use traffic factor forecast model, heat generator blast furnace gas use traffic is predicted.
2. heat generator blast furnace gas use traffic Forecasting Methodology according to claim 1, is characterized in that: described step 6, and concrete grammar is: according to training sample set S, adopt intelligent algorithm to build heat generator blast furnace gas use traffic factor forecast model; Based on production data, adopt intelligent regression modeling method to set up factor Model---the heat generator blast furnace gas use traffic factor forecast model embodying between heat generator blast furnace gas use traffic and heat generator blast furnace gas use traffic influence factor.
3. heat generator blast furnace gas use traffic Forecasting Methodology according to claim 2, is characterized in that: described training sample set S, is specifically configured to:
Wherein,
with
composition model input sample,
for model output sample,
represent P influence factor previous moment flow,
the heat generator blast furnace gas use traffic that represents previous moment,
the heat generator blast furnace gas use traffic that represents current time.
4. heat generator blast furnace gas use traffic Forecasting Methodology according to claim 1, is characterized in that: described step 7, and concrete grammar is:
A, the gas flow of influence factor and signal data are carried out to medium filtering and normalized;
B, utilize G-P algorithm to determine the embedding dimension m of each major influence factors;
C, utilize phase space to change to obtain training sample set S
i={ (x
j, y
j) | j=1,2 ..., n
i-m
i}:
Wherein, i represents sample set number, i, m, n ∈ Z, x
j∈ R
mirepresent the input of influence factor time series predicting model, y
j∈ R represents the output of model;
D, the Time Series Forecasting Methods of employing based on intelligent algorithm are set up each influence factor forecast model.
5. heat generator blast furnace gas use traffic Forecasting Methodology according to claim 1, is characterized in that: described step 8, specifically: utilize each major influence factors forecast model to obtain gas flow and the signal estimation value of each major influence factors; This flow and signal estimation value are inputed to heat generator blast furnace gas use traffic factor forecast model, obtain each prediction heat generator blast furnace gas use traffic variation prediction trend of correspondence constantly.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210199449.6A CN103514338A (en) | 2012-06-15 | 2012-06-15 | Method for predicting flow amount of blast furnace gas used by hot blast stove |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210199449.6A CN103514338A (en) | 2012-06-15 | 2012-06-15 | Method for predicting flow amount of blast furnace gas used by hot blast stove |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103514338A true CN103514338A (en) | 2014-01-15 |
Family
ID=49897054
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210199449.6A Pending CN103514338A (en) | 2012-06-15 | 2012-06-15 | Method for predicting flow amount of blast furnace gas used by hot blast stove |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103514338A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104182810A (en) * | 2014-09-09 | 2014-12-03 | 莱芜钢铁集团电子有限公司 | Dynamic gas balance control method |
| CN104408317A (en) * | 2014-12-02 | 2015-03-11 | 大连理工大学 | Metallurgy enterprise gas flow interval predicting method based on Bootstrap echo state network integration |
| CN105160195A (en) * | 2015-09-12 | 2015-12-16 | 内蒙古科技大学 | Blast furnace furnace-temperature predicating model and application thereof |
| CN103995987B (en) * | 2014-06-05 | 2017-02-15 | 中冶华天工程技术有限公司 | Heat efficiency analysis method for pulverized coal boiler with blending combustion of blast furnace gas |
| CN106779384A (en) * | 2016-12-07 | 2017-05-31 | 大连理工大学 | A kind of long-term interval prediction method of steel and iron industry blast furnace gas based on Information Granularity optimum allocation |
| CN110032555A (en) * | 2019-04-16 | 2019-07-19 | 上海建科工程咨询有限公司 | A kind of neural network tower crane Risk Forecast Method and system |
| CN112593032A (en) * | 2020-12-11 | 2021-04-02 | 安徽工业大学 | Key parameter processing method for blast furnace heat exchange air furnace |
| CN113251670A (en) * | 2021-05-28 | 2021-08-13 | 江苏永联慧科物联技术有限公司 | Hot blast stove control and training method, device, equipment, hot blast stove system and medium |
| CN115198047A (en) * | 2022-09-07 | 2022-10-18 | 宝信软件(南京)有限公司 | Hot blast stove combustion monitoring system and method based on big data analysis |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1557972A (en) * | 2004-01-16 | 2004-12-29 | 冶金自动化研究设计院 | Optimizing control method for hybrid type blast furnace hot blast stove |
| CN1766130A (en) * | 2005-12-09 | 2006-05-03 | 河北理工大学 | Harmonization control method for blast furnace hot blast stove system |
| KR20110047041A (en) * | 2009-10-29 | 2011-05-06 | 현대제철 주식회사 | Method for forecasting level of by-product gases holder |
| CN102109837A (en) * | 2009-12-24 | 2011-06-29 | 上海宝信软件股份有限公司 | Forecast and balance method for tank level of coke oven gas of steel makers |
-
2012
- 2012-06-15 CN CN201210199449.6A patent/CN103514338A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1557972A (en) * | 2004-01-16 | 2004-12-29 | 冶金自动化研究设计院 | Optimizing control method for hybrid type blast furnace hot blast stove |
| CN1766130A (en) * | 2005-12-09 | 2006-05-03 | 河北理工大学 | Harmonization control method for blast furnace hot blast stove system |
| KR20110047041A (en) * | 2009-10-29 | 2011-05-06 | 현대제철 주식회사 | Method for forecasting level of by-product gases holder |
| CN102109837A (en) * | 2009-12-24 | 2011-06-29 | 上海宝信软件股份有限公司 | Forecast and balance method for tank level of coke oven gas of steel makers |
Non-Patent Citations (1)
| Title |
|---|
| 梁青艳: "钢铁企业煤气供需动态预测问题的研究", 《万方学位论文》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103995987B (en) * | 2014-06-05 | 2017-02-15 | 中冶华天工程技术有限公司 | Heat efficiency analysis method for pulverized coal boiler with blending combustion of blast furnace gas |
| CN104182810A (en) * | 2014-09-09 | 2014-12-03 | 莱芜钢铁集团电子有限公司 | Dynamic gas balance control method |
| CN104408317A (en) * | 2014-12-02 | 2015-03-11 | 大连理工大学 | Metallurgy enterprise gas flow interval predicting method based on Bootstrap echo state network integration |
| CN105160195A (en) * | 2015-09-12 | 2015-12-16 | 内蒙古科技大学 | Blast furnace furnace-temperature predicating model and application thereof |
| CN106779384A (en) * | 2016-12-07 | 2017-05-31 | 大连理工大学 | A kind of long-term interval prediction method of steel and iron industry blast furnace gas based on Information Granularity optimum allocation |
| CN110032555A (en) * | 2019-04-16 | 2019-07-19 | 上海建科工程咨询有限公司 | A kind of neural network tower crane Risk Forecast Method and system |
| CN112593032A (en) * | 2020-12-11 | 2021-04-02 | 安徽工业大学 | Key parameter processing method for blast furnace heat exchange air furnace |
| CN112593032B (en) * | 2020-12-11 | 2024-03-26 | 安徽工业大学 | Key parameter processing method for blast furnace heat exchange air furnace |
| CN113251670A (en) * | 2021-05-28 | 2021-08-13 | 江苏永联慧科物联技术有限公司 | Hot blast stove control and training method, device, equipment, hot blast stove system and medium |
| CN115198047A (en) * | 2022-09-07 | 2022-10-18 | 宝信软件(南京)有限公司 | Hot blast stove combustion monitoring system and method based on big data analysis |
| CN115198047B (en) * | 2022-09-07 | 2022-12-09 | 宝信软件(南京)有限公司 | Hot blast stove combustion monitoring system and method based on big data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103514338A (en) | Method for predicting flow amount of blast furnace gas used by hot blast stove | |
| Wei et al. | A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy | |
| Wang et al. | Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems | |
| Clauß et al. | Control strategies for building energy systems to unlock demand side flexibility–A review | |
| Wang et al. | Review on implementation and assessment of conservation voltage reduction | |
| Oprea et al. | Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm | |
| US8065041B2 (en) | Concentrating control method of generating sets for grid energy saving and reduction of SO2 discharge | |
| WO2015039464A1 (en) | Global optimization scheduling strategy library based on timescale | |
| CN102444784B (en) | Pressure control system for steel enterprise steam pipe network based on dynamic matrix control | |
| CN102360181B (en) | Low-temperature heat real-time optimization system based on general algorithm sequential quadratic programming (GA-SQP) mixed optimization strategy | |
| CN105204333A (en) | Energy consumption prediction method for improving energy utilization rate of iron and steel enterprise | |
| CN103426035A (en) | Self-balanced byproduct blast furnace gas dispatching system in iron and steel industry and method for predicting yield and consumption | |
| CN102915006A (en) | Method for offline analyzing and online scheduling of byproduct gas of metallurgy industry | |
| CN101813941A (en) | Energy efficiency optimizing and dispatching system for cold, heat and electricity triple supply equipment | |
| CN104638636A (en) | Power daily load characteristic indicator prediction method | |
| CN105676820B (en) | City garden layered distribution type temperature control workload demand response control strategy | |
| CN114169236A (en) | Phase-change heat storage type electric heating negative control system control method based on LSTM algorithm | |
| CN103514486A (en) | Blast furnace gas incoming flow prediction method based on factor analysis | |
| CN102109837B (en) | Forecast and balance method for tank level of coke oven gas of steel makers | |
| Wu et al. | Modified shapley value-based profit allocation method for wind power accommodation and deep peak regulation of thermal power | |
| CN107609690B (en) | Method for optimizing load active management decision | |
| CN106011353B (en) | A kind of blast funnace hot blast stove air-fuel ratio self-optimization method | |
| CN102035210B (en) | Relaxative-constraint powerless equipment optimization method for power system | |
| Devriese et al. | Volt/VAr optimization with energy savings for distribution system using intelligent control | |
| CN115758763A (en) | Multi-energy flow system optimal configuration method and system considering source load uncertainty |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140115 |