CN104156776B - A kind of solar energy resources appraisal procedure - Google Patents
A kind of solar energy resources appraisal procedure Download PDFInfo
- Publication number
- CN104156776B CN104156776B CN201410163998.7A CN201410163998A CN104156776B CN 104156776 B CN104156776 B CN 104156776B CN 201410163998 A CN201410163998 A CN 201410163998A CN 104156776 B CN104156776 B CN 104156776B
- Authority
- CN
- China
- Prior art keywords
- data
- solar energy
- energy resources
- data collection
- collection
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000013480 data collection Methods 0.000 claims abstract description 32
- 238000009826 distribution Methods 0.000 claims abstract description 18
- 238000012216 screening Methods 0.000 claims abstract description 12
- 230000005540 biological transmission Effects 0.000 claims description 7
- 230000015572 biosynthetic process Effects 0.000 claims description 7
- 230000005855 radiation Effects 0.000 claims description 7
- 238000000205 computational method Methods 0.000 claims description 3
- 239000012141 concentrate Substances 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 238000003475 lamination Methods 0.000 claims description 3
- 230000004807 localization Effects 0.000 claims description 3
- 238000013316 zoning Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 9
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000012937 correction Methods 0.000 abstract description 3
- 238000013500 data storage Methods 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 5
- 238000013439 planning Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 230000001172 regenerating effect Effects 0.000 description 3
- 241001269238 Data Species 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- BYACHAOCSIPLCM-UHFFFAOYSA-N 2-[2-[bis(2-hydroxyethyl)amino]ethyl-(2-hydroxyethyl)amino]ethanol Chemical group OCCN(CCO)CCN(CCO)CCO BYACHAOCSIPLCM-UHFFFAOYSA-N 0.000 description 1
- 239000002028 Biomass Substances 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of solar energy resources appraisal procedure, methods described sets up region solar energy resources collection of illustrative plates including (1), obtains solar energy irradiance data collection S in region；(2) measured data collection Q is built；(3) data subset is built, the weight coefficient of each mesh point is calculated；(4) the following 24h of amendment numerical weather prediction model generation irradiance data collection SP, set up solar energy resources real-time distribution figure.The present invention considers the screening to measured data, can realize with website as few as possible to obtain more accurate region solar energy resources distribution, be installed and data storage resource with saving equipment.It is of the invention that correction factor is comprehensively calculated using linear related and weighted average, reduce amount of calculation, improve calculating speed.Real-time Monitoring Data is combined by the present invention with numerical weather forecast result, makes the result of assessment more accurate.
Description
Technical field
The present invention relates to a kind of method for evaluating the energy, in particular to a kind of method for evaluating solar energy source.
Background technology
Under the pressure of severe energy substitution situation and global warming, countries in the world are all sustainable developing
The clean energy resource of development as following energy development strategy, wherein solar energy with aboundresources, there is no region boundary line, cleaning etc.
Particular advantages and one of as focus of concern, various countries formulate the solar energy power generating developing goal of magnificence one after another.
China has a vast territory, solar energy resources very abundant.The more rich region of solar energy account for the 2/3 of area with
On, year amount of radiation more than 6,000,000,000 joules/square metre, the solar energy that annual earth's surface absorbs is about as much as 1.7 trillion tons of standard coals
Energy, with good Solar use condition.In order to promote the exploitation of regenerative resource, China formulates《China
People's republic's Renewable Energy Law》And the formal implementation in 1 day January in 2006.2007, country promulgated again《It is renewable
Energy Long-and Medium-term Development is planned》, propose to strive the year two thousand twenty, China's regenerative resource consumption figure is accounted for total energy consumption ratio
Reach 15%.The planning proposes specific developing goal to regenerative resources such as wind energy, biomass energy, solar energy, water energy.Its
In, the target of solar power generation is to reach on the basis of 300,000 kilowatts that the year two thousand twenty reaches 1,800,000 kilowatts in total capacity in 2010.
Real-time to solar energy resources monitoring and assessment can be that solar energy development planning and Electric Power Network Planning provide reliable foundation,
On the basis of monitoring collects conclusive resource data, the solar energy resources situation and characteristic distributions of assessment area, with reference to soil
Ground resource situation and local grid conditions, you can the problem of helping to find as early as possible in solar energy resources exploitation and bottleneck,
To formulate rational exploitation scale and Utilization plan, so that it is guaranteed that the economy that solar energy resources is developed is with actually may be used
Operability, it is ensured that solar energy power generating and the health coordinated development of power network.Solar energy resources observation station can monitor more system
Solar energy resources data, time scale is shorter, and data are more accurate, will provide data basis for photovoltaic power generation power prediction.For
This needs the appraisal procedure for providing the solar energy resources of a set of real-time monitoring and power prediction.
The content of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of solar energy resources appraisal procedure, and this method can be used for the sun
The planning and designing and photovoltaic plant operation and scheduling of energy photovoltaic plant.This method is first with mesoscale numerical weather forecast mould
Formula, obtained region solar energy resources distribution map is calculated with reference to measured datas such as historical measurement data, satellite datas；Then it is comprehensive
Consider region solar energy resources and light-metering website distribution situation, and region the feature, the applicable actual measurement of selection such as topography and geomorphology
Monitoring point.Finally the solar energy resources forecast result in region is modified using Real-time Monitoring Data, real-time change is obtained
Solar energy resources is distributed.
The purpose of the present invention is realized using following technical proposals：
A kind of solar energy resources appraisal procedure, it is theed improvement is that, methods described includes
(1) region solar energy resources collection of illustrative plates is set up, solar energy irradiance data collection S in region is obtained；
(2) measured data collection Q is built；
(3) data subset is built, the weight coefficient of each mesh point is calculated；
(4) the following 24h of amendment numerical weather prediction model generation irradiance data collection SP, set up solar energy resources real
When distribution map.
It is preferred that, the step (1) is included chi in global context, region terrain data and surface vegetation data input
Numerical weather prediction model is spent, localization numerical weather prediction model and selection parameter scheme, formation zone solar energy is set up
Irradiance distribution figure, obtains the solar energy irradiance data collection S in region.
Further, the Parameterization Scheme includes Microphysical scheme, long-wave radiation transmission plan, shortwave radiation
Transmission plan, skin lamination, land surface scheme, PBL scheme and Convective parameterization schemes.
It is preferred that, the step (2) includes data prediction and data screening.
Further, the data prediction includes
The photometric data that a, screening are collected；
B, reject or correct unreasonable data, obtain photometric data collection P.
Further, the step b includes
6.1) deletion error or exceptional value；
6.2) time with the presence of time shift is corrected as the following formula：
p(t)=p(t-m)；
Wherein, p (t) is the photometric data of t, and m is time shift length.
Further, the data screening comprises the steps；
7.1) correlation between measured data is calculated：
R in formulaxyFor relative coefficient, x, y are photometric data sequence,WithRespectively sequence x and y arithmetic average
Value；
7.2) data screening
rxy>=0.9, and two photometric data points distance be less than 10km when, then pick out data from photometric data collection P complete
Whole rate is relatively low, or the data sequence higher with other data sequence correlations；
7.3) generation measured data collection Q
Data sequence in data set P is screened, redundant data sequence is picked out, measured data collection Q is obtained.
It is preferred that, the step (3) includes
8.1) extracted in the solar energy irradiance data collection S obtained from step (1) with step (2) in measured data collection Q
The data subset S ' of grid where monitoring position coordinates, and the time interval of the data sequence in S ' concentrates corresponding data phase with Q
Together；
8.2) Q and S ' linear coefficient correlation A and deviation ratio B are asked for as the following formula,
S '=AQ+B
Wherein,N is the number of data sequence in measured data collection Q；
8.3) weight coefficient of m-th of mesh point in region is calculated as follows, computational methods are as follows：
Wherein ki=1/li, liIt is grid m center to the distance of each measured data position；
8.4) press step 8.3) zoning mesh point weight coefficient.
It is preferred that, the step (4) includes
9.1) each mesh point of numerical weather prediction model formation zone future 24h irradiance data SP is passed through；
9.2) S is correctedP(t) the solar energy resources distribution S of t, is obtainedP′(t).The irradiation of the t of m-th of mesh point
Spend and be：
SP' (m, t)=ka(m)SP(m,t)+kb(m)；
9.3) the irradiance distribution figure of drawing area.
Compared with the prior art, beneficial effects of the present invention are：
1st, The present invention gives the specific steps and flow assessed based on the solar energy resources monitored in real time, with very strong
Operability and application value.
2nd, the present invention considers the screening to measured data, can realize with website as few as possible more accurate to obtain
Region solar energy resources distribution, is installed and data storage resource with saving equipment.
3rd, the present invention is comprehensive calculates correction factor using linear related and weighted average, reduces amount of calculation, improves
Calculating speed.
4th, it is of the invention by Real-time Monitoring Data and numerical weather forecast knot compared with traditional solar energy resources appraisal procedure
Fruit is combined, and makes the result of assessment more accurate.
Brief description of the drawings
A kind of solar energy resources appraisal procedure flow chart that Fig. 1 provides for the present invention.
The mesoscale numerical weather forecast pattern WRF mode computation flow charts that Fig. 2 provides for the present invention.
Embodiment
The embodiment to the present invention is described in further detail below in conjunction with the accompanying drawings.
1st, region solar energy resources collection of illustrative plates is set up
By data input mesoscale climate model WRF patterns such as global context and landform, surface vegetations, by dropping twice
Yardstick, sets up localization numerical weather prediction model, selects suitable Parameterization Scheme, the history solar energy irradiation of formation zone
Distribution map is spent, the solar energy irradiance data collection S in the region is obtained.The calculation process of WRF patterns such as accompanying drawing 2, it is main in pattern
The Parameterization Scheme to be considered is as shown in table 1.
The Parameterization Scheme mainly considered in the pattern of table 1
| Parameterization Scheme title | |
| 1 | Microphysical scheme (mp_physics) |
| 2 | Long-wave radiation transmission plan (ra_lw_physics) |
| 3 | Shortwave radiation transmission plan (ra_sw_physics) |
| 4 | Skin lamination (sf_sfclay_physics) |
| 5 | Land surface scheme (sf_surface_physics) |
| 6 | PBL scheme (bl_pbl_physics) |
| 7 | Convective parameterization schemes (cu_physics) |
2nd, light simultaneous measurement data processing
1) data prediction
(1) photometric data being collected into is screened, marks unreasonable or abnormal data, and it is classified, it is unreasonable
Following three class is commonly divided into abnormal data：1) data are wrong caused by data acquisition mistake or data transmission channel failure
By mistake；2) equipment or communication failure and the improper shortage of data caused of preservation；3) due to data collecting card time calibration and setting
The data time shift that deviation is brought.
(2) reject and correct unreasonable data, finally give photometric data collection P, be broadly divided into the following steps：
A) deletion error and exceptional value；
B) to there are the data of time shift, time complexity curve is carried out：
P (t)=p (t-m)
Wherein p (t) is the photometric data of t, and m is time shift length.
Wherein, the percentage of head rice k of data after amendment is calculated：
If data integrity rate k<85%, or there is the continuous data missing more than 1 week, then data set is labeled
It is bright.
2) data screening
A) correlation between measured data is calculated
R in formulaxyFor relative coefficient, x, y are photometric data sequence,WithRespectively sequence x and y arithmetic mean of instantaneous value.
B) data screening
If rxy>=0.9, and the distance of two photometric data points is less than 10km, then is picked from photometric data collection P
Go out relatively low or higher with other data sequence correlations data sequence of data integrity rate.
C) generation measured data collection Q
After being screened to all data sequences in data set P, redundant data sequence is picked out, the reality of subsequent analysis is obtained
Survey data set Q.
3rd, solar energy resources real-time distribution figure is set up
1) correction factor is determined
A) extracted in the solar energy irradiance data collection S obtained from step 1 and monitoring position coordinates institute in measured data collection Q
Concentrate corresponding data identical with Q in the data subset S ' of grid, and the data sequence in S ' time interval.
B) Q and S ' linear coefficient correlation A and deviation ratio B are asked for, formula is as follows：
S '=AQ+B
Wherein,N is the number of data sequence in measured data collection Q.
C) in zoning m-th of mesh point weight coefficient, computational methods are as follows：
Wherein ki=1/li, liIt is grid m center to the distance of each measured data position.
D) repeat step (c), calculates the weight coefficient of all mesh points of whole region.
2) the solar energy resources real-time distribution of formation zone
A) each mesh point of WRF numerical weather prediction models formation zone future 24h irradiance data S is passed throughP。
B) to SP(t) it is modified, obtains the solar energy resources distribution S of tP′(t).The t of m-th of mesh point
Irradiation level is：
SP' (m, t)=ka(m)SP(m,t)+kb(m)；
C) according to the irradiation level result of each mesh point, the irradiance distribution figure of drawing area.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention rather than its limitations, to the greatest extent
The present invention is described in detail with reference to above-described embodiment for pipe, and those of ordinary skill in the art should be understood：Still
The embodiment of the present invention can be modified or equivalent, and without departing from any of spirit and scope of the invention
Modification or equivalent, it all should cover among scope of the presently claimed invention.
Claims (7)
1. a kind of solar energy resources appraisal procedure, it is characterised in that methods described includes
(1) region solar energy resources collection of illustrative plates is set up, solar energy irradiance data collection S in region is obtained；
(2) measured data collection Q is built；
(3) data subset is built, the weight coefficient of each mesh point is calculated；
(4) the following 24h of amendment numerical weather prediction model generation irradiance data collection SP, set up solar energy resources and divide in real time
Butut；
The step (3) includes
8.1) extracted in the solar energy irradiance data collection S obtained from step (1) with being monitored in measured data collection Q in step (2)
The data subset S ' of grid where position coordinates, and the time interval of the data sequence in S ' concentrates corresponding data identical with Q；
8.2) Q and S ' linear coefficient correlation A and deviation ratio B are asked for as the following formula,
S '=AQ+B
Wherein,N is the number of data sequence in measured data collection Q；
8.3) weight coefficient of m-th of mesh point in region is calculated as follows, computational methods are as follows：
Wherein ki=1/li, liIt is grid m center to the distance of each measured data position；
8.4) press step 8.3) zoning mesh point weight coefficient；
The step (4) includes
4.1) each mesh point of numerical weather prediction model formation zone future 24h irradiance data S is passed throughP；
4.2) S is correctedP(t) the irradiance distribution S of t, is obtainedP' (t), the irradiation level of the t of m-th of mesh point is：
SP' (m, t)=ka(m)SP(m,t)+kb(m)；
4.3) the irradiance distribution figure of drawing area.
2. a kind of solar energy resources appraisal procedure as claimed in claim 1, it is characterised in that the step (1) includes will be complete
Ball ambient field, region terrain data and surface vegetation data input mesoscale numerical weather forecast pattern, set up localization numerical value
Weather forecast pattern and selection parameter scheme, formation zone solar energy irradiance distribution figure obtain the solar energy irradiation in region
Degrees of data collection S.
3. a kind of solar energy resources appraisal procedure as claimed in claim 2, it is characterised in that the Parameterization Scheme includes micro-
Physical parameter scheme, long-wave radiation transmission plan, shortwave radiation transmission plan, skin lamination, land surface scheme, boundary layer
Scheme and Convective parameterization schemes.
4. a kind of solar energy resources appraisal procedure as claimed in claim 1, it is characterised in that the step (2) includes data
Pretreatment and data screening.
5. a kind of solar energy resources appraisal procedure as claimed in claim 4, it is characterised in that the data prediction includes
The photometric data that a, screening are collected；
B, reject or correct unreasonable data, obtain photometric data collection P.
6. a kind of solar energy resources appraisal procedure as claimed in claim 5, it is characterised in that the step b includes
6.1) deletion error or exceptional value；
6.2) time with the presence of time shift is corrected as the following formula：
P (t)=p (t-m)；
Wherein, p (t) is the photometric data of t, and m is time shift length.
7. a kind of solar energy resources appraisal procedure as claimed in claim 4, it is characterised in that the data screening includes following
Step；
7.1) correlation between measured data is calculated：
R in formulaxyFor relative coefficient, x, y are photometric data sequence,WithRespectively sequence x and y arithmetic mean of instantaneous value；
7.2) data screening
rxy>=0.9, and two photometric data points distance be less than 10km when, then pick out data integrity rate from photometric data collection P
It is relatively low, or the data sequence higher with other data sequence correlations；
7.3) generation measured data collection Q
Data sequence in data set P is screened, redundant data sequence is picked out, measured data collection Q is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410163998.7A CN104156776B (en) | 2014-04-23 | 2014-04-23 | A kind of solar energy resources appraisal procedure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410163998.7A CN104156776B (en) | 2014-04-23 | 2014-04-23 | A kind of solar energy resources appraisal procedure |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104156776A CN104156776A (en) | 2014-11-19 |
| CN104156776B true CN104156776B (en) | 2017-07-14 |
Family
ID=51882273
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410163998.7A Active CN104156776B (en) | 2014-04-23 | 2014-04-23 | A kind of solar energy resources appraisal procedure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104156776B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893732A (en) * | 2015-01-19 | 2016-08-24 | 赵明智 | Assessment method of solar energy resources |
| CN106156453B (en) * | 2015-03-24 | 2020-01-03 | 国家电网公司 | Solar energy resource assessment method based on numerical weather forecast data |
| CN106156906A (en) * | 2015-03-26 | 2016-11-23 | 中国能源建设集团新疆电力设计院有限公司 | A kind of solar energy resources analyzing evaluation method for design of photovoltaic power station |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102073791A (en) * | 2011-01-12 | 2011-05-25 | 东南大学 | Local solar energy resource abundance evaluating system for design of photovoltaic power station |
| CN103455730A (en) * | 2013-09-23 | 2013-12-18 | 东南大学 | Distributed photovoltaic power generating capacity estimating system and solar radiation data generation method |
| CN103617452A (en) * | 2013-08-14 | 2014-03-05 | 国家电网公司 | Photometry network layout method in large-scale photovoltaic base area |
-
2014
- 2014-04-23 CN CN201410163998.7A patent/CN104156776B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102073791A (en) * | 2011-01-12 | 2011-05-25 | 东南大学 | Local solar energy resource abundance evaluating system for design of photovoltaic power station |
| CN103617452A (en) * | 2013-08-14 | 2014-03-05 | 国家电网公司 | Photometry network layout method in large-scale photovoltaic base area |
| CN103455730A (en) * | 2013-09-23 | 2013-12-18 | 东南大学 | Distributed photovoltaic power generating capacity estimating system and solar radiation data generation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104156776A (en) | 2014-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Doljak et al. | Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia | |
| CN101916337B (en) | Method for dynamically predicting potential productivity of paddy rice based on geographical information system | |
| Zheng et al. | An overview of global ocean wind energy resource evaluations | |
| CN104517037B (en) | A kind of remote sensing estimation method of ecosystem carrying capacity | |
| CN106295899B (en) | Wind power probability density Forecasting Methodology based on genetic algorithm Yu supporting vector quantile estimate | |
| Nasrollahi et al. | The greenhouse technology in different climate conditions: A comprehensive energy-saving analysis | |
| CN112765800A (en) | Design method of distributed water resource configuration model | |
| CN107316095A (en) | A kind of region meteorological drought grade prediction technique for coupling multi-source data | |
| Liu et al. | Optimization of planning structure in irrigated district considering water footprint under uncertainty | |
| CN104156776B (en) | A kind of solar energy resources appraisal procedure | |
| El Alimi et al. | Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia | |
| CN112036093A (en) | Land hydrologic coupling model | |
| CN104616215A (en) | Energy efficiency comprehensive evaluation method for thermal power plant | |
| CN112819254B (en) | Climate mode prediction method for assimilating planet scale and machine learning exocoercion | |
| CN104599087A (en) | Transmission line patrol judgment method | |
| Citakoglu et al. | Determination of monthly wind speed of Kayseri region with Gray estimation method | |
| Hao et al. | A Remote‐Sensing Ecological Index Approach for Restoration Assessment of Rare‐Earth Elements Mining | |
| Ma et al. | Comprehensive review of food-energy-water nexus at the community scale | |
| Ammar et al. | Photovoltaic water pumping system site suitability analysis using AHP GIS method in Southern Algeria | |
| CN105488346A (en) | Spatial prediction analogy method of large-scale land cover change | |
| Paktinat et al. | Solar desalination plant site suitability through composing decision-making systems and fuzzy logic in Iran (using the desert areas approach) | |
| CN114925944B (en) | Prediction method for underground water level recovery amount | |
| CN102542511B (en) | Method for optimizing normal storage water level of hydropower station | |
| Fasipe et al. | GIS and RS Techniques for Identification of Hydropower Potential Sites in Benin-Owena River Basin in Nigeria | |
| Nasehi et al. | Modelling site selection for solar power establishment by fuzzy logic and ordered weighted averaging methods in arid and semi-arid regions (Case study Yazd province-IRAN) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |