CN110288130A - A kind of user power consumption prediction technique - Google Patents
A kind of user power consumption prediction technique Download PDFInfo
- Publication number
- CN110288130A CN110288130A CN201910469792.XA CN201910469792A CN110288130A CN 110288130 A CN110288130 A CN 110288130A CN 201910469792 A CN201910469792 A CN 201910469792A CN 110288130 A CN110288130 A CN 110288130A
- Authority
- CN
- China
- Prior art keywords
- electricity consumption
- user
- user power
- power consumption
- variable
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000005611 electricity Effects 0.000 claims abstract description 63
- 239000000203 mixture Substances 0.000 claims abstract description 19
- 239000004615 ingredient Substances 0.000 claims description 4
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Probability & Statistics with Applications (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of user power consumption prediction techniques, comprising steps of 1) combining the influences of the factors to power demand such as user property, external condition, establish the Finite mixture model for being equipped with adjoint variable；2) Finite mixture model established according to step 1), deconstructs the electricity consumption data of certain zone user；3) total electricity consumption of certain zone user is deconstructed, obtains electricity consumption desired value, complete user power consumption prediction；4) utilize relative error detecting step 3) in total electricity consumption destructing correctness and stability.Compared with prior art, the present invention can carry out the calculating of different user power demand probability by adjoint variable, facilitate the prediction of user power utilization demand in auxiliary area, can effectively reflect the actual change trend of electricity.
Description
Technical field
The present invention relates to electric power market demand forecast analysis technical fields, more particularly, to a kind of user power consumption prediction side
Method.
Background technique
Electric energy is widely used in industry, agricultural, enterprises and institutions and daily life, is national economy and people
The people's livelihood indispensable energy living.However, we are while enjoying electric energy and bringing us and facilitate and is bright, also in moment wave
Take electric energy.For researching and analysing for user power utilization demand, there are selection per capita disposable income, user power utilization valence in the prior art
Lattice and last user's per capita household electricity consumption establish the error correction prediction model of current period resident living power utility demand as the factor；
There are also be based on Panel Data Model, the residence using the integrated electric appliance index voluntarily calculated as explanatory variable to Typical Cities in China
Civilian electricity carries out detailed prediction；And the analysis based on power demand influence factor, resident is directed to using polynary preference pattern
The buying behavior for buying household electrical appliance carries out probability Estimation, and then predicts household electrical appliance and the potential electricity consumption level of domestic lighting.
However the research of existing resident living power utility demand does not directly deconstruct electric power big data to analyze, does not consider that user belongs to
The influences of the factors to power demand such as property, external condition, i.e. user's difference power demand probability how with above-mentioned variable change
The power demand probability results changed and changed, therefore obtain cannot reflect the actual change trend of electricity well.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of user power consumption is pre-
Survey method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of user power consumption prediction technique, includes the following steps:
Step 1: the influence in conjunction with factors such as user property, external conditions to power demand establishes and is equipped with adjoint variable
Finite mixture model；
The expression formula of the Finite mixture model of foundation are as follows:
In formula, z is adjoint variable, i.e., the probability of variant power demand distribution will change with the variation of z, λi(z)
For probability of i-th of Gaussian Profile under the conditions of z, λiIt (z) is load multivariable logit process, K is the quantity of distribution, θiIt is every
The parameter that a kind of user power utilization returns, y is explained variable, and x is explanatory variable, f (yi| x, θi) it is ingredient gauss of distribution function.
λi(z) expression formula are as follows:
Step 2: the Finite mixture model established according to step 1 deconstructs the electricity consumption data of certain zone user.
The expression formula that the electricity consumption data of certain zone user is deconstructed are as follows:
f(yijt|xt,θij)=
θji0+θji1TEMPERATUREt+θji2RAINt+θji3HUMIDt+θji4WINDt+εji
In formula, λij(zt) it include temperature, weather conditions that WEATHER is represented, DATE, TIME respectively represent different electricity consumption days
Phase and moment, PEOPLE represent personnel's flow mobile data and current total electricity consumption Y, f (yijt|xt,θij) it include temperature
TEMPERATUREt, rainfall RAINt, humidity HUMIDt, wind speed WINDtImpact factor, vjiIt is jth item electric appliance under i state
Desired value, εjiFor residual error of the jth item electric appliance under i state, meet normal distribution.
Step 3: the total electricity consumption of certain zone user being deconstructed, electricity consumption desired value is obtained, it is pre- to complete user power consumption
It surveys.
Each electrical appliance is predicted according to current total electricity consumption Y and current meteorology, date, time information, it will
Itemize electricity consumption yjyIt indicates are as follows:
The main input variable of above formula is current total electricity consumption Yt, weather and time variable；
The probability that the different conditions of jth item electric appliance are calculated separately according to above formula goes out to work as in conjunction with the probability calculation of different conditions
The electricity consumption desired value of jth item electric appliance under preceding Y total electricity consumption, and then complete the decomposition to current electricity consumption total amount.
Step 4: utilizing relative error detecting step 3 correctness and stability of total electricity consumption destructing in).
The expression formula of relative error are as follows:
In formula, N is electrical appliance sum, and T is data volume length.
Compared with prior art, the invention has the following advantages that
One, the method for the present invention deconstructs electric power big data to analyze, in conjunction with factors pair such as user property, external conditions
The influence of power demand obtains the prediction result of electricity consumption total amount, can effectively reflect the actual change trend of electricity；
Two, present invention introduces the Finite mixture models for having adjoint variable, and wherein adjoint variable will determine that user's difference is used
The classification foundation of electricity demanding, and the calculating of different user power demand probability can be carried out by adjoint variable, help to assist
The prediction of user power utilization demand in region, facilitates the rational of electricity market annual planning, avoids the waste of resource.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.Obviously, described embodiment is this
A part of the embodiment of invention, rather than whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art exist
Every other embodiment obtained under the premise of creative work is not made, all should belong to the scope of protection of the invention.
Finite mixture model assumes that observation sample is to be distributed the mixed distribution collectively constituted by multiple.Specially system F is by K
A distribution collectively forms, and wherein K is obtained by data-driven judgement.Gaussian Mixture distribution is collectively constituted by K Gaussian Profile
Mixed distribution form, expression formula are as follows:
Wherein, F (K, λ, μ, σ) is the mixed distribution situation observed, f (μi,σi) it is ingredient gauss of distribution function, λiFor
Each is independently distributed the probability of appearance, wherein λi> 0, andMarron and Wand (1992) [32] was once demonstrate,proved
Bright, any one continuous probability distribution can be fitted by limited Gaussian Profile, therefore the analysis of mixed model
As a result it will not change with the distribution form of observation and change.
Observation Distribution value can be divided into multiple Gaussian Profiles by Finite mixture model, therefore it is substantially a kind of classification side
Formula.When carrying out power demand research, not only need to study the power demand classification of user, it is also necessary to understand user property, outer
Influence of the factors such as boundary's condition to power demand.
As shown in Figure 1, the present invention relates to a kind of user power consumption prediction technique, this method includes the following steps:
Step 1: the influence in conjunction with factors such as user property, external conditions to power demand, establishes Finite mixture model,
Its citation form is as follows:
In formula, z is adjoint variable, i.e., the probability of variant power demand distribution will change with the variation of z, andIn the present invention, it is believed that λ (z)iFor load multivariable logit process, it may be assumed that
In formula, λiIt (z) is probability of i-th of Gaussian Profile under the conditions of z.
Further, it is a kind of commonly to the expansion mode of Finite mixture model be finite mixtures are distributed in F (K, λ, μ, σ)
Expanding is regression forms, it may be assumed that
Wherein, y is explained variable, and x is explanatory variable, θiThe parameter returned for every a kind of user power utilization.The formula is
The citation form that finite mixtures return thinks that observation y is the condition distribution based on x.
It can be according to observation other factors for it on the basis of finite mixtures return in conjunction with two kinds of mode of extensions
Adjoint variable is added in the influence for returning ingredient probability, i.e., willIt is write as:
Step 2: being deconstructed using FMM model to business premises data.
When carrying out Construction analysis to the subitem electricity consumption of business premises using Finite mixture model, this report assumes commercial building
The jth kind subitem electricity consumption of space is in t moment electricity consumption yj,tBe be made of K kind state, while the probability of different electricity conditions by
Date, temperature, climate condition variable z are influenced, therefore above formula can be rewritten are as follows:
Above formula is the Finite mixture model for the jth item electrical appliance t moment electricity consumption of building, θijIndicate the electricity consumption of jth item
The electricity consumption y of i-th kind of state of deviceijtWith influence factor xtRelationship；λ is probability corresponding with jth item electrical appliance, and z is and the
The corresponding influence factor of j electrical appliances.Therefore, according to different dates, temporal characteristics, electrical appliance j has different state structures
It is proportional, therefore have many characteristics, such as different temperature sensitivities, cause its electricity consumption different.
Since the electricity consumption of different time has notable difference to business premises in 1 year, even if to same weather
Feedback degree is also different, as night is different from feedback degree of the daytime for this variable that rains；Different states goes out
Existing probability is also influenced by various factors, such as day off and festivals or holidays, since the concentration of crowd is different, leads to difference
The electricity consumption of building increases different；Even in one day, the power demand of different time is also different, and office building is often in the afternoon
Low power consumption is entered after 5 points, and often welcome peak of power consumption etc. in the evening of market on weekdays；Likewise, in synchronization
The probability that different weather the state of electricity consumption may also occur has an impact.It therefore can be by f (yijt|xt,θij) and λij(zt)
It is write as:
f(yijt|xt,θij)=
θji0+θji1TEMPERATUREt+θji2RAINt+θji3HUMIDt+θji4WINDt+εji
Wherein, λij(zt) primary variables include temperature, weather conditions that WEATHER is represented, DATE and TIME are represented
Different electricity consumption dates and moment, PEOPLE represent personnel's flow mobile data and current total electricity consumption Y, f (yijt|
xt,θij) mainly include temperature, rainfall, humidity, wind speed impact factor.In addition, vjiFor phase of the jth item electric appliance under i state
Prestige value, εjiFor residual error of the jth item electric appliance under i state, meet normal distribution.
Step 3: being deconstructed to total electricity consumption.
Destructing to total electricity consumption is substantially according to current total electricity consumption Y and current meteorology, date, moment letter
Breath predicts each electrical appliance.It therefore can be by ignition distributor demand yjIt indicates are as follows:
yj=E (yj|Y,z,x,βj,θj,Kj)
Wherein, yjIt is the electricity consumption of certain electrical appliance j, Y is current total electricity consumption, and x is the influence factor of state, and z is to influence
The variable of different conditions probability of occurrence, βj,θjWith for model parameter, KjFor the state sum of j electrical appliance.Wherein βj,θj,KjBy
The influence of the factors such as attribute, position, technical level, the user's composition of building.For same building, these parameters are to maintain
Constant, therefore subitem electricity consumption can be indicated are as follows:
Therefore the main input variable of above formula is current total electricity consumption Y, weather and time variable, calculates separately out electricity consumption
The different conditions of device j, by combining the probability of different conditions that can calculate the electricity consumption phase of the j electric appliance under current Y total electricity consumption
Prestige value, to realize the decomposition to current electricity consumption total amount.
Step 4: the calculating of destructing error.
The correctness and stability deconstructed using relative error come expression model, defines the relative error of each electrical appliance
Are as follows:
Overall error are as follows:
Wherein, N is electrical appliance sum, and T is data volume length.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
The staff for being familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (6)
1. a kind of user power consumption prediction technique, which is characterized in that this method includes the following steps:
1) influences of the factors to power demand such as user property, external condition are combined, the finite mixtures for being equipped with adjoint variable are established
Model；
2) Finite mixture model established according to step 1), deconstructs the electricity consumption data of certain zone user；
3) total electricity consumption of certain zone user is deconstructed, obtains electricity consumption desired value, complete user power consumption prediction；
4) utilize relative error detecting step 3) in total electricity consumption destructing correctness and stability.
2. a kind of user power consumption prediction technique according to claim 1, which is characterized in that in step 1), foundation has
Limit the expression formula of mixed model are as follows:
In formula, z is adjoint variable, i.e., the probability of variant power demand distribution will change with the variation of z, λiIt (z) is i-th
Probability of a Gaussian Profile under the conditions of z, λiIt (z) is load multivariable logit process, K is the quantity of distribution, θiFor every one kind
The parameter that user power utilization returns, y is explained variable, and x is explanatory variable, f (yi|x,θi) it is ingredient gauss of distribution function.
3. a kind of user power consumption prediction technique according to claim 2, which is characterized in that in step 1), λi(z) table
Up to formula are as follows:
4. a kind of user power consumption prediction technique according to claim 2, which is characterized in that in step 1), to certain region
The expression formula that the electricity consumption data of user is deconstructed are as follows:
f(yijt|xt,θij)=θji0+θji1TEMPERATUREt+θji2RAINt+θji3HUMIDt+θji4WINDt+εji
In formula, λij(zt) include WEATHER represent temperature, weather conditions, DATE, TIME respectively represent the different electricity consumption dates and
Moment, PEOPLE represent personnel's flow mobile data and current total electricity consumption Y, f (yijt|xt,θij) it include temperature
TEMPERATUREt, rainfall RAINt, humidity HUMIDt, wind speed WINDtImpact factor, vjiIt is jth item electric appliance under i state
Desired value, εjiFor residual error of the jth item electric appliance under i state, meet normal distribution.
5. a kind of user power consumption prediction technique according to claim 4, which is characterized in that in step 1), step 3)
Particular content are as follows:
Each electrical appliance is predicted according to current total electricity consumption Y and current meteorology, date, time information, will be itemized
Electricity consumption yjtIt indicates are as follows:
The main input variable of above formula is current total electricity consumption Yt, weather and time variable；
The probability that the different conditions of jth item electric appliance are calculated separately according to above formula, in conjunction with the probability calculations of different conditions, to go out current Y total
The electricity consumption desired value of jth item electric appliance under electricity consumption, and then complete the decomposition to current electricity consumption total amount.
6. a kind of user power consumption prediction technique according to claim 5, which is characterized in that in step 4), relative error
Expression formula are as follows:
In formula, N is electrical appliance sum, and T is data volume length.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910469792.XA CN110288130B (en) | 2019-05-31 | 2019-05-31 | User electricity consumption prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910469792.XA CN110288130B (en) | 2019-05-31 | 2019-05-31 | User electricity consumption prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110288130A true CN110288130A (en) | 2019-09-27 |
| CN110288130B CN110288130B (en) | 2024-04-30 |
Family
ID=68003045
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910469792.XA Active CN110288130B (en) | 2019-05-31 | 2019-05-31 | User electricity consumption prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110288130B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111027202A (en) * | 2019-12-04 | 2020-04-17 | 北京软通智城科技有限公司 | Method, device and equipment for predicting digital city and storage medium |
| CN111506636A (en) * | 2020-05-12 | 2020-08-07 | 上海积成能源科技有限公司 | System and method for analyzing residential electricity consumption behavior based on autoregressive and neighbor algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140358838A1 (en) * | 2013-06-04 | 2014-12-04 | International Business Machines Corporation | Detecting electricity theft via meter tampering using statistical methods |
| CN104268769A (en) * | 2014-03-11 | 2015-01-07 | 南方电网科学研究院有限责任公司 | Time-of-use electricity price design method based on model clustering method |
| CN105512768A (en) * | 2015-12-14 | 2016-04-20 | 上海交通大学 | User electricity consumption relevant factor identification and electricity consumption quantity prediction method under environment of big data |
| CN105844351A (en) * | 2016-03-22 | 2016-08-10 | 广西电网有限责任公司电力科学研究院 | Prosperity index prediction method for electric power consumption market |
-
2019
- 2019-05-31 CN CN201910469792.XA patent/CN110288130B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140358838A1 (en) * | 2013-06-04 | 2014-12-04 | International Business Machines Corporation | Detecting electricity theft via meter tampering using statistical methods |
| CN104268769A (en) * | 2014-03-11 | 2015-01-07 | 南方电网科学研究院有限责任公司 | Time-of-use electricity price design method based on model clustering method |
| CN105512768A (en) * | 2015-12-14 | 2016-04-20 | 上海交通大学 | User electricity consumption relevant factor identification and electricity consumption quantity prediction method under environment of big data |
| CN105844351A (en) * | 2016-03-22 | 2016-08-10 | 广西电网有限责任公司电力科学研究院 | Prosperity index prediction method for electric power consumption market |
Non-Patent Citations (1)
| Title |
|---|
| 窦健 等: "结构化用电客户互动需求信息的比对库设计", 《电力系统及其自动化学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111027202A (en) * | 2019-12-04 | 2020-04-17 | 北京软通智城科技有限公司 | Method, device and equipment for predicting digital city and storage medium |
| CN111027202B (en) * | 2019-12-04 | 2023-12-15 | 北京软通绿城科技有限公司 | Digital city prediction method, device, equipment and storage medium |
| CN111506636A (en) * | 2020-05-12 | 2020-08-07 | 上海积成能源科技有限公司 | System and method for analyzing residential electricity consumption behavior based on autoregressive and neighbor algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110288130B (en) | 2024-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Song et al. | Water resources utilization efficiency and influence factors under environmental restrictions | |
| Tam et al. | Thermal insulation and cost effectiveness of green-roof systems: An empirical study in Hong Kong | |
| Chuai et al. | Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China | |
| Ang | Monitoring changes in economy-wide energy efficiency: from energy–GDP ratio to composite efficiency index | |
| Dai et al. | A multistage irrigation water allocation model for agricultural land-use planning under uncertainty | |
| Sun et al. | To irrigate or not to irrigate: Analysis of green roof performance via a vertically-resolved hygrothermal model | |
| Zhao et al. | Energy consumption quota of public buildings based on statistical analysis | |
| Yang et al. | Natural capital utilization based on a three-dimensional ecological footprint model: A case study in northern Shaanxi, China | |
| Duan et al. | Valuing the impact of climate change on China’s economic growth | |
| Ma et al. | Overview and progress of Chinese geographical human settlement research | |
| CN105260803A (en) | Power consumption prediction method for system | |
| CN104156786B (en) | A kind of maximum daily load prediction system of the nonworkdays for considering meteorological multifactor impact | |
| Wang et al. | Integrated simulation of the dualistic water cycle and its associated processes in the Haihe River Basin | |
| CN105608333B (en) | A kind of meteorological sensitive electricity method for digging for considering multizone difference | |
| CN110288130A (en) | A kind of user power consumption prediction technique | |
| Wu et al. | The elasticity of the potential of emission reduction to energy saving: Definition, measurement, and evidence from China | |
| CN111401732B (en) | Hierarchical identification method for water resource supply and demand situation | |
| Wang et al. | Temporal-spatial variation analysis of agricultural biomass and its policy implication as an alternative energy in northeastern China | |
| Zhang et al. | Ecological and economic analyses of the forest metabolism system: a case study of Guangdong Province, China | |
| Liu et al. | Spatiotemporal assessment of water security in China: An integrated supply-demand coupling model | |
| Meng et al. | Structure construction, evolution analysis and sustainability evaluation of Water-Ecological-Economic system | |
| CN104134102B (en) | Long-term electricity needs distribution forecasting method in power network based on LEAP models | |
| Setiawan et al. | Sustainable cities research and sustainable management modelling: Indonesia and world trend | |
| Gu et al. | Land use structure optimization based on uncertainty fractional joint probabilistic chance constraint programming | |
| Chao et al. | Concept and connotation of water resources carrying capacity in water ecological civilization construction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Su Yun Inventor after: Zhang Qiqi Inventor after: Shi Zhixiong Inventor after: Song Yan Inventor after: Pang Tianyu Inventor after: Shen Quanjiang Inventor after: Wu Libo Inventor after: Zhou Yang Inventor after: Ma Rong Inventor after: Shi Zhengyu Inventor after: Chen Wei Inventor after: Guo Naiwang Inventor after: Tian Yingjie Inventor after: Qu Haini Inventor before: Su Yun Inventor before: Zhang Qiqi Inventor before: Shi Zhixiong Inventor before: Song Yan Inventor before: Pang Tianyu Inventor before: Shen Quanjiang Inventor before: Wu Libo Inventor before: Zhou Yang Inventor before: Ma Rong Inventor before: Shi Zhengyu Inventor before: Chen Wei Inventor before: Guo Naiwang Inventor before: Tian Yingjie Inventor before: Qu Haini |
|
| GR01 | Patent grant |