CN111861260A - Regional energy economic operation and energy efficiency analysis method and terminal based on energy big data - Google Patents
Regional energy economic operation and energy efficiency analysis method and terminal based on energy big data Download PDFInfo
- Publication number
- CN111861260A CN111861260A CN202010752497.8A CN202010752497A CN111861260A CN 111861260 A CN111861260 A CN 111861260A CN 202010752497 A CN202010752497 A CN 202010752497A CN 111861260 A CN111861260 A CN 111861260A
- Authority
- CN
- China
- Prior art keywords
- energy
- regional
- consumption
- economic
- node
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 41
- 238000005265 energy consumption Methods 0.000 claims abstract description 77
- 238000009826 distribution Methods 0.000 claims abstract description 36
- 238000010276 construction Methods 0.000 claims abstract description 34
- 238000004364 calculation method Methods 0.000 claims abstract description 24
- 238000013178 mathematical model Methods 0.000 claims abstract description 12
- 230000005540 biological transmission Effects 0.000 claims description 60
- 238000004519 manufacturing process Methods 0.000 claims description 59
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 48
- 238000012546 transfer Methods 0.000 claims description 28
- 239000003245 coal Substances 0.000 claims description 24
- 239000003345 natural gas Substances 0.000 claims description 24
- 238000003860 storage Methods 0.000 claims description 21
- 238000000034 method Methods 0.000 claims description 19
- 230000005611 electricity Effects 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000004804 winding Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 238000007728 cost analysis Methods 0.000 claims description 6
- -1 electric power Substances 0.000 claims description 6
- 239000003208 petroleum Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000004146 energy storage Methods 0.000 claims description 3
- 238000011158 quantitative evaluation Methods 0.000 claims description 3
- 238000009776 industrial production Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 16
- 238000011160 research Methods 0.000 abstract description 3
- 238000011234 economic evaluation Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000000739 chaotic effect Effects 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000013501 data transformation Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002955 isolation Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000010409 thin film Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
-
- 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
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a regional energy economic operation and energy efficiency analysis method and a terminal based on energy big data, which are implemented by constructing a regional energy basic information data system; collecting data related to energy consumption in the region, and constructing an energy distribution mathematical model; constructing a domain energy index calculation model; and obtaining regional energy and economic data. Based on the big data of energy, the regional energy economic operation is comprehensively analyzed by integrating multiple dimensions such as regional energy distribution, enterprise energy utilization condition, energy transportation mode, regional economic development condition and the like. And a comprehensive information platform can be formed, and the economic operation and energy efficiency of regional energy can be analyzed. The relation between energy consumption and regional economic growth is specifically researched through the quantitative relation, a research foundation is laid for constructing an information platform for regional energy economic evaluation, and a theoretical foundation is provided for information construction of smart cities, smart parks and credit systems.
Description
Technical Field
The invention relates to the field of big data calculation and energy application, in particular to a regional energy economic operation and energy efficiency analysis method and a terminal based on energy big data.
Background
The growth of the regional economy has obvious characteristics of high consumption and high emission, and the development of the economic society is increasingly subjected to double constraints of energy and environment. The sustainable development concept is widely advocated in China after global administrative counterthinking and practice exploration, and China is accelerating to change the economic development mode and taking the building resource-saving and environment-friendly society as a starting point and a footfall point. The relationship between economic development and energy consumption is the important point for realizing sustainable development, and the comprehensive evaluation and research on the sustainable development of the regional energy-economic system is the basis and the premise for making the sustainable development decision of the regional economic society.
The energy-economic system is a multi-level multi-factor dynamic open complex system, the energy-economic systems conflict with each other and are mutually coordinated, and the whole system continuously exchanges substances, energy and information with the outside under the action of internal coordination and external control. According to the dissipation structure theory, once the change of a certain parameter of the unbalanced system reaches a certain threshold value, the system can be changed from a chaotic disordered state into a collaborative ordered state in time, space or function, so that the coordinated development of the chaotic disordered state and the collaborative ordered state is realized.
At the moment of the rising of the sustainable development call, the contradiction and conflict between the economic development and the energy consumption are increasingly prominent, and the method becomes a bottleneck problem to be solved urgently for realizing the sustainable development in an area.
At present, data collection and learning of regional energy economic operation and energy efficiency analysis are only collected manually, some data related to energy consumption in a region are collected and processed and analyzed manually, and even if a considerable amount of data or more comprehensive data is collected, the collected data cannot be organically combined to obtain regional energy economic data because a construction means and a construction model for the energy consumption data are lacked. The regional energy economic operation cannot be comprehensively analyzed and known from multiple dimensions, a comprehensive informatization platform cannot be formed, regional energy basic information data is lost, finally obtained conclusions are deviated, evaluation is carried out only by using a single index, statistical data are incomplete and untimely, and regional energy economic operation and energy efficiency analysis are affected.
Disclosure of Invention
The regional energy economic operation and energy efficiency analysis method based on the energy big data is provided, and the regional energy economic operation is comprehensively analyzed in a multi-dimensional mode such as regional energy distribution, enterprise energy utilization conditions, energy transportation modes and regional economic development conditions on the basis of the energy big data.
The method comprises the following steps:
step one, constructing a regional energy basic information data system;
step two, constructing an energy distribution mathematical model;
step three, constructing a domain energy index calculation model;
constructing a regional energy production, transfer and consumption distribution map, and constructing an energy distribution economic elasticity coefficient based on energy construction cost, energy transmission cost and energy loss; calculating correlation coefficients of energy and output values in various industries and total contribution degrees of unit production values in economic composition of various energy regions based on the energy consumption ratio, regional economic condition and industry ratio of the integrated regional enterprises;
and step four, displaying the system data.
It should be further noted that the first step further includes:
the regional energy basic information data system comprises two dimensions of energy information and economic information;
the energy information includes: energy profile, proportion analysis and cost analysis are 3 types, and the energy profile comprises: total energy production, total energy consumption, total electricity production, and total electricity consumption;
the proportion analysis comprises the following steps: coal production capacity ratio, oil production capacity ratio, natural gas production capacity ratio, electric power production capacity ratio, coal consumption capacity ratio, oil consumption capacity ratio, natural gas consumption capacity ratio, electric power consumption capacity ratio, unit production total value energy consumption, enterprise unit added value energy consumption above the scale, unit production total value electric energy consumption and processing conversion efficiency;
the cost analysis comprises the following steps: energy construction costs, electricity construction costs, coal construction costs, natural gas construction costs, oil construction costs, energy storage costs, electricity storage costs, coal storage costs, natural gas storage costs, oil storage costs, energy delivery costs, electricity delivery costs, coal delivery costs, natural gas delivery costs, and oil delivery costs;
the economic information includes: general overview, economic status, energy efficiency class 3;
the general overview includes: the total production value of the region, the investment sum of fixed assets of the whole society, the total industrial production value and the retail total sum of social consumer goods;
the economic conditions include: the total production value proportion of the first industry in the area, the second industry in the area, the third industry in the area, the per capita total production value, the total contribution rate of the industrial total assets, the profit rate of the industrial cost and the profit rate of the regional production;
the energy efficiency includes: the unit energy GDP contribution value, the unit electric power GDP contribution value, the unit coal GDP contribution value, the unit natural gas GDP contribution value and the unit oil GDP contribution value.
It should be further noted that the second step further includes:
generating a regional energy production, transfer and consumption distribution map according to the basic information data, constructing an energy production, transportation, transfer, storage and consumption link association model, integrating regional coal, electric power, natural gas, petroleum and other energy construction costs, energy transmission costs and energy loss influence factors, and establishing an energy distribution mathematical model;
the energy transmission network is usually in a radial structure, and is regarded as a tree with a system side power supply as a root node and an energy consumption center as a leaf node;
there are three types of nodes for the simplified model of energy delivery networks:
a node formed by an energy transmission center;
a node formed by the energy consumption center;
nodes formed by energy transfer stations;
there are three types of edges:
the method includes the steps that firstly, edges are formed by connecting lines among nodes of the energy transmission center;
the edge is formed by connecting the energy transmission center node and the energy transfer station node;
the edge is formed by connecting the energy transmission center node and the energy consumption center node;
for the node formed by the energy transmission center, the weight is given as 0; for the nodes formed by the energy consumption centers, the weight is the energy consumption; for nodes formed by the energy transfer stations, the weight is the total energy amount fed back to the energy transmission network by the energy transfer stations;
the energy transmission center node is connected with the energy transmission center node to form an edge, and the weight is mainly determined according to whether the branch has a transformer or not; the weight of the branch with the transformer is set to be 0.5, and the weight of the branch without the transformer is set to be 0;
when a three-winding transformer is configured in the energy transmission network, the energy transmission network is split into 2 double-winding transformers, and the weight is set to be 0.25; the weight of an edge formed by connecting the energy transmission center node and the DG node is set to be 0;
the edge with the weight value of 0 has the highest priority, and can be searched firstly in the island searching process;
the weight is determined by the energy consumption size connected with the edge and the importance of the energy consumption; the determination of the weight of the edges is a multi-index quantitative evaluation, which comprises 2 indexes of energy consumption and importance of energy types, and the weight can be obtained through a formula; the weight of a specific edge is obtained by mainly calculating 2 indexes in the formula (1);
the formula (2) is to standardize all the energy consumption, convert the energy consumption into numerical values between 1 and 10 and simultaneously not change the proportional relation between the numerical values;
ωi＝λ1LNiλ2Si(1)
in the formula: omegaiThe weight value of the ith edge is; lambda [ alpha ]1、λ2Taking lambda as the proportion of each index in the weight1＝λ2＝1；LmaxThe maximum value of the total energy source in the region; l isminThe minimum value of the total energy source in the region; l isiThe energy consumption amount of the ith node is; l isNiThe standard value is the energy consumption amount of the ith node; siThe invention is an importance index of the ith energy consumption type, the energy consumption types are divided into 1 st, 2 nd and 3 rd types, the corresponding S values are 1, 2 nd and 3 rd, wherein the most important is the 1 st type energy consumption;
the energy consumption center node is processed as follows: the proportion of controllable and uncontrollable energy consumption centers in the energy consumption centers connected with the energy consumption center nodes is assumed to be respectively equal to (a + b is equal to 1; a is more than or equal to 0, and b is more than or equal to 0);
if a>0, an additional node is arranged to lead out a controllable energy consumption center connected with the L, the additional node is connected with the original node through a virtual line containing a switch, and the energy consumption center is a PL(ii) a The energy consumption center connected with the original energy consumption center node becomes a pure uncontrollable energy consumption center b PL。
It should be further noted that step three further includes: establishing an energy distribution economic elasticity coefficient calculation model;
firstly, establishing an energy distribution economic elastic coefficient calculation mathematical model according to an established energy production, transportation, transfer, storage and consumption link correlation model, and integrating the influence factors such as regional coal, electric power, natural gas, petroleum and other energy construction costs, energy transmission costs, energy loss and the like;
based on an energy transmission network undirected tree model and energy transmission cost and constraint conditions, the following region energy distribution economic elastic coefficient calculation model is adopted:
xv＝1,v∈G
xv＝{0,1},v∈V\G
in the formula: n is the maximum number of the nodes in the tree, and the number of the root node is 0; omegavAssigning a value to the node v; v is a set of nodes in the tree; g is a set of nodes of the energy transfer stations in the tree; stThe total number of the energy types is k; dvFor the distribution of energy in various energy regions,
energy losses in various regions, including transportation losses, transportation costs, and unit energy construction costs; lv,gRepresents all nodes on the chain between node v and node g;
Qvlower, upper and actual values of allowed energy delivery or demand, respectively, for the v node; a isvIs a parent node of node v;
are respectively a branchvV the amplitude of the various types of energy actually delivered and the maximum energy delivery allowed for it.
It should be further noted that step three further includes: establishing a regional energy efficiency coefficient calculation model;
based on the energy consumption ratio, the regional economic condition and the industry ratio of the integrated regional enterprises, the correlation coefficient of the energy and the output value in each industry and the total contribution degree of unit production in the regional economic composition of various energy sources are calculated, and a regional energy efficiency coefficient calculation model is established as follows:
wherein Q iscThe energy efficiency coefficient of the regional energy is obtained, and m is the type number of energy requirements in a regional range; x is the number ofiContribution of the total economic quantity, σ, to the corresponding class i energyiIs the weight, xi, of the industry in which the corresponding i-type energy source is locatedmThe maximum value of the economic production value, mu, of various energy sources in each industrymThe method is an index evaluation value which is optimal for the industry,
is an industry average index evaluation result, omegajThe proportion of the corresponding j industry to the energy consumption, n is the total number of the related enterprise types, and yjAnd contributing values to GDP of corresponding j industrial unit energy.
The invention also provides a terminal for realizing the regional energy economic operation and energy efficiency analysis method, which comprises the following steps: the storage is used for storing a computer program and a regional energy economic operation and energy efficiency analysis method; and the processor is used for executing the computer program and the regional energy economic operation and energy efficiency analysis method so as to realize the steps of the regional energy economic operation and energy efficiency analysis method.
According to the technical scheme, the invention has the following advantages:
according to the regional energy economic operation and energy efficiency analysis method based on the energy big data, a regional energy basic information data system is constructed; collecting data related to energy consumption in the region, and constructing an energy distribution mathematical model; constructing a domain energy index calculation model; and obtaining regional energy and economic data. Based on the big data of energy, the regional energy economic operation is comprehensively analyzed by integrating multiple dimensions such as regional energy distribution, enterprise energy utilization condition, energy transportation mode, regional economic development condition and the like. And a comprehensive information platform can be formed, and the economic operation and energy efficiency of regional energy can be analyzed.
The invention also constructs a regional energy basic information data system, constructs a regional energy production, transfer and consumption distribution map, and constructs an energy distribution economic elasticity coefficient based on energy construction cost, energy transmission cost, energy loss and the like; meanwhile, energy consumption ratio, regional economic condition and industry ratio of regional enterprises are integrated, correlation coefficients of energy and output values in various industries and total contribution of unit production values in regional economic composition of various energy resources are calculated, regional energy efficiency coefficients are constructed, and a regional energy economic operation and energy efficiency analysis system is established. The relation between energy consumption and regional economic growth is specifically researched through the quantitative relation, a research foundation is laid for constructing an information platform for regional energy economic evaluation, and a theoretical foundation is provided for information construction of smart cities, smart parks and credit systems.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a regional energy economy operation and energy efficiency analysis method based on energy big data;
fig. 2 is a framework diagram of regional energy economy operation and energy efficiency analysis.
Detailed Description
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The invention provides a regional energy economic operation and energy efficiency analysis method based on energy big data. As shown in fig. 1 and 2.
S1, constructing a regional energy basic information data system; the regional energy distribution and the economy are evaluated by adopting big data analysis, and the economic conditions and the energy consumption information of each industry in the regional range are acquired, processed and analyzed by utilizing big data, Internet of things and the like on the basis of regional economic development and energy consumption analysis, so that a regional energy basic information data system is comprehensively formed.
S2, constructing an energy distribution mathematical model; the data preprocessing is mainly used for supporting data training modeling, preprocessing of consistency, integrity, accuracy and the like is performed on original data by using a big data technology, and meanwhile, data cleaning is completed, wherein the cleaning mode can adopt missing value processing, noise data processing and the like; data specification, the data specification mode can adopt dimension specification, quantity specification, data compression and the like; the data transformation and the data transformation mode can adopt preprocessing work such as smoothing, data aggregation, normalization and the like.
S3, constructing a domain energy index calculation model; constructing a regional energy production, transfer and consumption distribution map, and constructing an energy distribution economic elasticity coefficient based on energy construction cost, energy transmission cost, energy loss and the like; meanwhile, energy consumption ratio, regional economic condition and industry ratio of regional enterprises are integrated, correlation coefficient of energy and output value in each industry and total contribution degree of unit production value in regional economic composition of various energy resources are calculated, and regional energy efficiency coefficient is constructed.
And S4, displaying the system data. Based on a big data platform, a visual self-service analysis tool is applied to configure the content of the application scene, so as to realize the analysis and display of the scene.
The following describes the implementation of the present invention in detail:
1. construction of regional energy basic information data system
1.1, forming a basic information data system of the regional energy-economic system;
the invention adopts a theoretical analysis method to optimize a regional energy-economic system basic information data system by adopting an expert investigation method. According to the set content and principle of the comprehensive evaluation of the regional energy-economic system, the comprehensive basic information data system of the regional energy-economic system can be determined into four levels: the target layer is the comprehensive level of sustainable development of a regional energy-economy-environment system, the element layer comprises an energy system and an economic system, the criterion layer comprises the themes of the structure, the total amount, the benefit, the speed, the location and the like of each element, and the index layer is an index for reflecting each criterion of each element. The preliminarily constructed comprehensive evaluation index system of the regional energy-economy-environment system also needs to be screened and optimized.
The system comprises two dimensions of energy information and economic information, wherein the energy information comprises: energy profile, proportion analysis and cost analysis are 3 types, and the energy profile comprises: the method comprises the following steps of (1) analyzing the total energy production amount, the total energy consumption amount, the total power production amount and the total power consumption amount by proportion, wherein the analysis comprises the following steps: coal production capacity ratio, oil production capacity ratio, natural gas production capacity ratio, electric power production capacity ratio, coal consumption capacity ratio, oil consumption capacity ratio, natural gas consumption capacity ratio, electric power consumption capacity ratio, unit production total value energy consumption, enterprise unit increment value energy consumption above the scale, unit production total value electric energy consumption and processing conversion efficiency, wherein the cost analysis comprises the following steps: energy construction cost, electric power construction cost, coal construction cost, natural gas construction cost, oil construction cost, energy storage cost, electric power storage cost, coal storage cost, natural gas storage cost, oil storage cost, energy transmission cost, electric power transmission cost, coal transmission cost, natural gas transmission cost, and oil transmission cost; the economic information includes: general overview, economic status, energy efficiency class 3, the general overview includes: the total production value of the region, the investment sum of fixed assets of the whole society, the total industrial value and the retail total amount of social consumer goods, and the economic conditions comprise: the total value proportion of the first industry in the area, the total value proportion of the second industry in the area, the total value proportion of the third industry in the area, the total value of the per capita area, the total contribution rate of the industrial total assets, the profit rate of the industrial cost and the expense, the total value growth rate of the area production, and the energy efficiency comprises: unit energy GDP contribution value, unit electric power GDP contribution value, unit coal GDP contribution value, unit natural gas GDP contribution value and unit oil GDP contribution value.
2. Constructing an energy distribution mathematical model;
firstly, a regional energy production, transfer and consumption distribution map is generated according to basic information data, an energy production, transportation, transfer, storage and consumption link association model is constructed, influence factors such as regional coal, electric power, natural gas and petroleum energy construction cost, energy transmission cost and energy loss are integrated, and an energy distribution mathematical model is established.
The energy transmission network usually adopts a radial structure, and can be regarded as a tree with a system side power supply as a root node and an energy consumption center as a leaf node. There are three types of nodes for the simplified model of energy delivery networks: a node formed by an energy transmission center; a node formed by the energy consumption center; and thirdly, nodes formed by the energy transfer stations. There are three types of edges: the method includes the steps that firstly, edges are formed by connecting lines among nodes of the energy transmission center; the edge is formed by connecting the energy transmission center node and the energy transfer station node; and the edge is formed by connecting the energy transmission center node and the energy consumption center node. The three types of edges and vertexes have different forming modes and different setting algorithms for the weights. For the node formed by the energy transmission center, the weight is given as 0; for the nodes formed by the energy consumption centers, the weight is the energy consumption; for the nodes formed by the energy transfer stations, the weight is the total energy amount fed back to the energy transmission network by the energy transfer stations.
The energy transmission center node and the energy transmission center node are connected to form an edge, and the weight is mainly determined according to whether the branch has a transformer or not. The branch weight with the transformer is set to 0.5, and the branch weight without the transformer is set to 0. The energy transmission network may encounter the situation of a three-winding transformer, and the energy transmission network can be split into 2 double-winding transformers, the weight is set to be 0.25, and the priority of the weight is higher than that of the double-winding transformer because the work efficiency of the energy transmission network is higher than that of the double-winding transformer. And the weight of the edge formed by connecting the energy transmission central node and the DG node is set to be 0. The edge with the weight of 0, whose priority is the highest, is searched first in the island search process.
The weight value of the edge formed by connecting the energy transmission central node and the energy consumption central node can be determined by the energy consumption size connected with the edge and the importance of the energy consumption. The determination of the weight of the edges is actually a multi-index quantitative evaluation, and comprises 2 indexes of energy consumption and importance of energy types, and the weight can be obtained through a formula. The weight of the specific edge is obtained by calculating 2 indexes in the formula (1). The formula (2) is used for standardizing all the energy consumption, converting the energy consumption into numerical values between 1 and 10 and simultaneously not changing the proportional relation between the numerical values.
ωi＝λ1LNiλ2Si(1)
In the formula: omegaiThe weight value of the ith edge is; lambda [ alpha ]1、λ2Taking lambda as the proportion of each index in the weight1＝λ2＝1；LmaxThe maximum value of the total energy source in the region; l isminThe minimum value of the total energy source in the region; l isiThe energy consumption amount of the ith node is; l isNiThe standard value is the energy consumption amount of the ith node; siThe invention is an importance index of the ith energy consumption type, the energy consumption types are divided into 1 st, 2 nd and 3 rd types, the corresponding S values are 1, 2 nd and 3 rd, and the most important is the energy consumption of the 1 st type.
Considering the controllability and the uncontrollable performance of energy consumption, the energy consumption center node is processed as follows: the proportion of controllable energy consumption centers to uncontrollable energy consumption centers in the energy consumption centers connected with the energy consumption center nodes is assumed to be equal to (a + b is equal to 1; a is more than or equal to 0, and b is more than or equal to 0). If a>0, an additional node is arranged to lead out a controllable energy consumption center connected with the L, the additional node is connected with the original node through a virtual line containing a switch, and the energy consumption center is a PL(ii) a The energy consumption center connected with the original energy consumption center node becomes a pure uncontrollable energy consumption center b PL。
3. Constructing a domain energy index calculation model;
3.1 building an energy distribution economic elasticity coefficient calculation model.
The enterprise production efficiency power index calculation model is characterized in that firstly, an energy distribution economic elasticity coefficient calculation mathematical model is established according to the established energy production, transportation, transfer, storage and consumption link association model and the influence factors such as regional coal, electric power, natural gas, petroleum and the like energy construction cost, energy transmission cost, energy loss and the like. Based on the undirected tree model of the energy transmission network and the energy transmission cost and constraint conditions, the following economic elastic coefficient calculation model of regional energy distribution is adopted:
xv＝1,v∈G
xv＝{0,1},v∈V\G
in the formula: n is the maximum number of the nodes in the tree, and the number of the root node is 0; omegavAssigning a value to the node v; v is a set of nodes in the tree; g is a set of nodes of the energy transfer stations in the tree; stThe total number of the energy types is k; dvFor the distribution of energy in various energy regions,
energy losses in various regions, including transportation losses, transportation costs, and unit energy construction costs; lv,gRepresents all nodes on the chain between node v and node g;
Qvlower, upper and actual values of allowed energy delivery or demand, respectively, for the v node; a isvIs a section ofA parent node of point v;
are respectively a branchvV the amplitude of the various types of energy actually delivered and the maximum energy delivery allowed for it.
And 3.2, establishing a region energy efficiency coefficient calculation model.
According to the regional energy efficiency coefficient calculation method, regional enterprise energy consumption ratio, regional economic condition and industry ratio are integrated, correlation coefficients of energy and production values in various industries and total contribution of unit production values in regional economic composition of various energy are calculated, and a regional energy efficiency coefficient calculation model is established as follows:
wherein Q iscThe energy efficiency coefficient of the regional energy is obtained, and m is the type number of energy requirements in a regional range; x is the number ofiContribution of the total economic quantity, σ, to the corresponding class i energyiIs the weight, xi, of the industry in which the corresponding i-type energy source is locatedmThe maximum value of the economic production value, mu, of various energy sources in each industrymThe method is an index evaluation value which is optimal for the industry,
is an industry average index evaluation result, omegajThe proportion of the corresponding j industry to the energy consumption, n is the total number of the related enterprise types, and yjAnd contributing values to GDP of corresponding j industrial unit energy.
4. And (5) implementing scene application deployment.
Based on a big data platform, a visual self-service analysis tool is applied to configure the content of the application scene, so as to realize the analysis and display of the scene.
4.1 overall architecture;
the energy big data center basic platform is composed of three parts, namely electric power internal data, electric power external data and safety protection equipment.
(1) Power internal data;
the internal data of the electric power is stored in a company data center, and is processed by a digital operation center to form a digital product, and the digital product is transmitted to an energy big data center through an isolating device to be released.
(2) Power external data;
external data are transmitted into a government and enterprise data sharing platform through data import, database synchronization, user filling and the like, are transmitted to a digital operation center through an isolation device, are processed into digital products, and are transmitted back to an energy big data center through the isolation device to be released.
(3) Safety protection equipment;
the safety protection strategy is synchronously implemented in the construction process of the energy big data center, the safety protection of the energy big data center platform and the service application borne by the energy big data center platform is realized, a controllable safety protection system is constructed, and the safe and stable operation of various services and data of the energy big data center is guaranteed.
4.2 technical route;
the platform technical route follows the layered design idea of 'front-end micro-application big report, middle-end data sharing service and rear-end virtualization support', and is wholly based on a micro-service micro-application architecture. The hardware layer constructs a resource pool by using virtualization and other technologies to provide services for the upper layer. The data layer is constructed according to two layers of architectures, namely a source end data storage layer and a market layer (analysis result data). The service layer provides convenient data sharing service for users through an API interface and the like. The application layer comprises four functional modules of a data catalogue, a data application product supermarket, interactive communication and platform operation. The presentation layer comprises document reports, web portals, large-screen presentations, WeChat small programs and other publishing forms.
Based on the method, the invention also provides a terminal for implementing the regional energy economic operation and energy efficiency analysis method based on the energy big data, which comprises the following steps: the storage is used for storing a computer program and a regional energy economic operation and energy efficiency analysis method based on energy big data; and the processor is used for executing the computer program and the regional energy economic operation and energy efficiency analysis method based on the energy big data so as to realize the steps of the regional energy economic operation and energy efficiency analysis method based on the energy big data.
The terminal may be implemented in various forms. For example, the terminal described in the embodiments of the present invention may include a mobile terminal such as a mobile phone, a notebook computer, a Personal Digital Assistant (PDA), a tablet computer (PAD), and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like. It will be understood by those skilled in the art that the configuration according to the embodiment of the present invention can be applied to a fixed type terminal in addition to elements particularly used for moving purposes.
The terminal may include a wireless communication unit, an audio/video (a/V) input unit, a user input unit, a sensing unit, an output unit, a memory, an interface unit, a controller, and a power supply unit, etc. It is to be understood that not all illustrated components are required to be implemented. More or fewer components may alternatively be implemented.
The terminals may enable wired or wireless communication, which is the transmission and/or reception of radio signals to and/or from at least one of a base station (e.g., access point, node B, etc.), an external terminal, and a server via electrical signals. Such radio signals may include voice call signals, video call signals, or various types of data transmitted and/or received according to text and/or multimedia messages.
The terminal may be provided with a display unit. For example, when the terminal is in a process of implementing regional energy economic operation and energy efficiency analysis method based on energy big data, the display unit may display a related User Interface (UI) or Graphical User Interface (GUI). The display unit may also display images and/or received images, a UI or GUI showing video or images and related functions, and the like.
Meanwhile, when the display unit and the touch panel are stacked on each other in the form of layers to form a touch screen, the display unit may be used as an input device and an output device. The display unit may include at least one of a Liquid Crystal Display (LCD), a Thin Film Transistor LCD (TFT-LCD), an Organic Light-Emitting Diode (OLED) display, a flexible display, a three-dimensional (3D) display, and the like. Some of these displays may be configured to be transparent to allow a user to see from the outside, which may be referred to as transparent displays, and a typical transparent display may be, for example, a Transparent Organic Light Emitting Diode (TOLED) display or the like. Depending on the particular desired implementation, the terminal may include two or more display units (or other display devices), for example, the terminal may include an external display unit and an internal display unit. The touch screen may be used to detect a touch input pressure as well as a touch input position and a touch input area.
The terminal can be implemented in a computer-readable medium using, for example, computer software, hardware, or any combination thereof. For hardware implementation, the embodiments described herein may be implemented using at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein, and in some cases, such embodiments may be implemented in a controller. For a software implementation, the implementation such as a process or a function may be implemented with a separate software module that allows performing at least one function or operation. The software codes may be implemented by software applications (or programs) written in any suitable programming language, which may be stored in memory and executed by the controller.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A regional energy economic operation and energy efficiency analysis method based on energy big data is characterized by comprising the following steps:
step one, constructing a regional energy basic information data system;
step two, constructing an energy distribution mathematical model;
step three, constructing a domain energy index calculation model;
constructing a regional energy production, transfer and consumption distribution map, and constructing an energy distribution economic elasticity coefficient based on energy construction cost, energy transmission cost and energy loss; calculating correlation coefficients of energy and output values in various industries and total contribution degrees of unit production values in economic composition of various energy regions based on the energy consumption ratio, regional economic condition and industry ratio of the integrated regional enterprises;
and step four, displaying the system data.
2. The energy efficiency operation and energy efficiency analysis method based on energy big data according to claim 1,
the first step further comprises the following steps:
the regional energy basic information data system comprises two dimensions of energy information and economic information;
the energy information includes: energy profile, proportion analysis and cost analysis are 3 types, and the energy profile comprises: total energy production, total energy consumption, total electricity production, and total electricity consumption;
the proportion analysis comprises the following steps: coal production capacity ratio, oil production capacity ratio, natural gas production capacity ratio, electric power production capacity ratio, coal consumption capacity ratio, oil consumption capacity ratio, natural gas consumption capacity ratio, electric power consumption capacity ratio, unit production total value energy consumption, enterprise unit added value energy consumption above the scale, unit production total value electric energy consumption and processing conversion efficiency;
the cost analysis comprises the following steps: energy construction costs, electricity construction costs, coal construction costs, natural gas construction costs, oil construction costs, energy storage costs, electricity storage costs, coal storage costs, natural gas storage costs, oil storage costs, energy delivery costs, electricity delivery costs, coal delivery costs, natural gas delivery costs, and oil delivery costs;
the economic information includes: general overview, economic status, energy efficiency class 3;
the general overview includes: the total production value of the region, the investment sum of fixed assets of the whole society, the total industrial production value and the retail total sum of social consumer goods;
the economic conditions include: the total production value proportion of the first industry in the area, the second industry in the area, the third industry in the area, the per capita total production value, the total contribution rate of the industrial total assets, the profit rate of the industrial cost and the profit rate of the regional production;
the energy efficiency includes: the unit energy GDP contribution value, the unit electric power GDP contribution value, the unit coal GDP contribution value, the unit natural gas GDP contribution value and the unit oil GDP contribution value.
3. The energy efficiency operation and energy efficiency analysis method based on energy big data according to claim 1,
the second step further comprises:
generating a regional energy production, transfer and consumption distribution map according to the basic information data, constructing an energy production, transportation, transfer, storage and consumption link association model, integrating regional coal, electric power, natural gas, petroleum and other energy construction costs, energy transmission costs and energy loss influence factors, and establishing an energy distribution mathematical model;
the energy transmission network is usually in a radial structure, and is regarded as a tree with a system side power supply as a root node and an energy consumption center as a leaf node;
there are three types of nodes for the simplified model of energy delivery networks:
a node formed by an energy transmission center;
a node formed by the energy consumption center;
nodes formed by energy transfer stations;
there are three types of edges:
the method includes the steps that firstly, edges are formed by connecting lines among nodes of the energy transmission center;
the edge is formed by connecting the energy transmission center node and the energy transfer station node;
the edge is formed by connecting the energy transmission center node and the energy consumption center node;
for the node formed by the energy transmission center, the weight is given as 0; for the nodes formed by the energy consumption centers, the weight is the energy consumption; for nodes formed by the energy transfer stations, the weight is the total energy amount fed back to the energy transmission network by the energy transfer stations;
the energy transmission center node is connected with the energy transmission center node to form an edge, and the weight is mainly determined according to whether the branch has a transformer or not; the weight of the branch with the transformer is set to be 0.5, and the weight of the branch without the transformer is set to be 0;
when a three-winding transformer is configured in the energy transmission network, the energy transmission network is split into 2 double-winding transformers, and the weight is set to be 0.25; the weight of an edge formed by connecting the energy transmission center node and the DG node is set to be 0;
the edge with the weight value of 0 has the highest priority, and can be searched firstly in the island searching process;
the weight is determined by the energy consumption size connected with the edge and the importance of the energy consumption; the determination of the weight of the edges is a multi-index quantitative evaluation, which comprises 2 indexes of energy consumption and importance of energy types, and the weight can be obtained through a formula; the weight of a specific edge is obtained by mainly calculating 2 indexes in the formula (1);
the formula (2) is to standardize all the energy consumption, convert the energy consumption into numerical values between 1 and 10 and simultaneously not change the proportional relation between the numerical values;
ωi＝λ1LNiλ2Si(1)
in the formula: omegaiThe weight value of the ith edge is; lambda [ alpha ]1、λ2Taking lambda as the proportion of each index in the weight1＝λ2＝1；LmaxThe maximum value of the total energy source in the region; l isminThe minimum value of the total energy source in the region; l isiThe energy consumption amount of the ith node is; l isNiThe standard value is the energy consumption amount of the ith node; siThe invention is an importance index of the ith energy consumption type, the energy consumption types are divided into 1 st, 2 nd and 3 rd types, the corresponding S values are 1, 2 nd and 3 rd, wherein the most important is the 1 st type energy consumption;
the energy consumption center node is processed as follows: the proportion of controllable and uncontrollable energy consumption centers in the energy consumption centers connected with the energy consumption center nodes is assumed to be respectively equal to (a + b is equal to 1; a is more than or equal to 0, and b is more than or equal to 0);
if a>0, an additional node is arranged to lead out a controllable energy consumption center connected with the L, the additional node is connected with the original node through a virtual line containing a switch, and the energy consumption center is a PL(ii) a The energy consumption center connected with the original energy consumption center node becomes a pure uncontrollable energy consumption center b PL。
4. The energy efficiency operation and energy efficiency analysis method based on energy big data according to claim 1,
the third step also comprises: establishing an energy distribution economic elasticity coefficient calculation model;
firstly, establishing an energy distribution economic elastic coefficient calculation mathematical model according to an established energy production, transportation, transfer, storage and consumption link correlation model, and integrating the influence factors such as regional coal, electric power, natural gas, petroleum and other energy construction costs, energy transmission costs, energy loss and the like;
based on an energy transmission network undirected tree model and energy transmission cost and constraint conditions, the following region energy distribution economic elastic coefficient calculation model is adopted:
xv＝1,v∈G
xv＝{0,1},v∈V\G
in the formula: n is the maximum number of the nodes in the tree, and the number of the root node is 0; omegavAssigning a value to the node v; v is a set of nodes in the tree; g is a set of nodes of the energy transfer stations in the tree; stThe total number of the energy types is k; dvFor the distribution of energy in various energy regions,
energy losses in various regions, including transportation losses, transportation costs, and unit energy construction costs; lv,gRepresents all nodes on the chain between node v and node g;
Qvpermissible energy supply to the v-nodes in each caseOr lower, upper and actual values of demand; a isvIs a parent node of node v;
are respectively a branchvV the amplitude of the various types of energy actually delivered and the maximum energy delivery allowed for it.
5. The energy efficiency operation and energy efficiency analysis method based on energy big data according to claim 1,
the third step also comprises: establishing a regional energy efficiency coefficient calculation model;
based on the energy consumption ratio, the regional economic condition and the industry ratio of the integrated regional enterprises, the correlation coefficient of the energy and the output value in each industry and the total contribution degree of unit production in the regional economic composition of various energy sources are calculated, and a regional energy efficiency coefficient calculation model is established as follows:
wherein Q iscThe energy efficiency coefficient of the regional energy is obtained, and m is the type number of energy requirements in a regional range; x is the number ofiContribution of the total economic quantity, σ, to the corresponding class i energyiIs the weight, xi, of the industry in which the corresponding i-type energy source is locatedmThe maximum value of the economic production value, mu, of various energy sources in each industrymThe method is an index evaluation value which is optimal for the industry,
is an industry average index evaluation result, omegajThe proportion of the corresponding j industry to the energy consumption, n is the total number of the related enterprise types, and yjAnd contributing values to GDP of corresponding j industrial unit energy.
6. A terminal for realizing regional energy economic operation and energy efficiency analysis method is characterized by comprising the following steps:
the storage is used for storing a computer program and a regional energy economic operation and energy efficiency analysis method;
a processor for executing the computer program and the regional energy economy running and energy efficiency analyzing method to realize the steps of the regional energy economy running and energy efficiency analyzing method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010752497.8A CN111861260A (en) | 2020-07-30 | 2020-07-30 | Regional energy economic operation and energy efficiency analysis method and terminal based on energy big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010752497.8A CN111861260A (en) | 2020-07-30 | 2020-07-30 | Regional energy economic operation and energy efficiency analysis method and terminal based on energy big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111861260A true CN111861260A (en) | 2020-10-30 |
Family
ID=72945729
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010752497.8A Pending CN111861260A (en) | 2020-07-30 | 2020-07-30 | Regional energy economic operation and energy efficiency analysis method and terminal based on energy big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111861260A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112365175A (en) * | 2020-11-17 | 2021-02-12 | 杭州安恒信息安全技术有限公司 | Enterprise area-based economic analysis method, system, storage medium and equipment |
| CN112700083A (en) * | 2020-11-30 | 2021-04-23 | 北京中电普华信息技术有限公司 | Method and device for constructing scene of resource comprehensive utilization and service index system |
| CN112884347A (en) * | 2021-03-11 | 2021-06-01 | 清华大学 | Urban energy balance management system and construction method thereof |
| CN115809783A (en) * | 2022-12-09 | 2023-03-17 | 国网江苏省电力有限公司扬州供电分公司 | Method and device for evaluating and predicting industry-divided energy efficiency of medium-and-large-sized energy users |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110106321A1 (en) * | 2009-11-03 | 2011-05-05 | Spirae, Inc. | Dynamic distributed power grid control system |
| WO2014057304A1 (en) * | 2012-10-10 | 2014-04-17 | New Jersey Institute Of Technology | Decentralized controls and communications for autonomous distribution networks in smart grid |
| CN103824128A (en) * | 2014-02-24 | 2014-05-28 | 华南理工大学 | Method for scale prediction of regional distributed type comprehensive energy-supply system |
| WO2015167048A1 (en) * | 2014-04-30 | 2015-11-05 | 옴니시스템 주식회사 | Adaptive method for predicting demand for energy |
| US20160281497A1 (en) * | 2015-03-26 | 2016-09-29 | Peter Tilke | Probabalistic modeling and analysis of hydrocarbon-containing reservoirs |
| CN106779157A (en) * | 2016-11-18 | 2017-05-31 | 中铁第勘察设计院集团有限公司 | The Forecasting Methodology of Regional Energy consumption demand |
| CN108233367A (en) * | 2018-01-22 | 2018-06-29 | 国网山东省电力公司寿光市供电公司 | The optimal isolated island containing distributed power distribution network divides computational methods |
| CN109214655A (en) * | 2018-08-13 | 2019-01-15 | 国网河南省电力公司经济技术研究院 | Construction method of power grid construction level and economic structure and acceleration evaluation index system thereof |
| CN110232478A (en) * | 2019-06-12 | 2019-09-13 | 哈尔滨学院 | A kind of analysis of social economy prediction technique |
| CN110689240A (en) * | 2019-09-12 | 2020-01-14 | 国网山东省电力公司寿光市供电公司 | Fuzzy comprehensive evaluation method for economic operation of power distribution network |
-
2020
- 2020-07-30 CN CN202010752497.8A patent/CN111861260A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110106321A1 (en) * | 2009-11-03 | 2011-05-05 | Spirae, Inc. | Dynamic distributed power grid control system |
| WO2014057304A1 (en) * | 2012-10-10 | 2014-04-17 | New Jersey Institute Of Technology | Decentralized controls and communications for autonomous distribution networks in smart grid |
| CN103824128A (en) * | 2014-02-24 | 2014-05-28 | 华南理工大学 | Method for scale prediction of regional distributed type comprehensive energy-supply system |
| WO2015167048A1 (en) * | 2014-04-30 | 2015-11-05 | 옴니시스템 주식회사 | Adaptive method for predicting demand for energy |
| US20160281497A1 (en) * | 2015-03-26 | 2016-09-29 | Peter Tilke | Probabalistic modeling and analysis of hydrocarbon-containing reservoirs |
| CN106779157A (en) * | 2016-11-18 | 2017-05-31 | 中铁第勘察设计院集团有限公司 | The Forecasting Methodology of Regional Energy consumption demand |
| CN108233367A (en) * | 2018-01-22 | 2018-06-29 | 国网山东省电力公司寿光市供电公司 | The optimal isolated island containing distributed power distribution network divides computational methods |
| CN109214655A (en) * | 2018-08-13 | 2019-01-15 | 国网河南省电力公司经济技术研究院 | Construction method of power grid construction level and economic structure and acceleration evaluation index system thereof |
| CN110232478A (en) * | 2019-06-12 | 2019-09-13 | 哈尔滨学院 | A kind of analysis of social economy prediction technique |
| CN110689240A (en) * | 2019-09-12 | 2020-01-14 | 国网山东省电力公司寿光市供电公司 | Fuzzy comprehensive evaluation method for economic operation of power distribution network |
Non-Patent Citations (1)
| Title |
|---|
| 李虎威 等: "中国能源消费强度地区差异分解及影响因素分析", 中国矿业, vol. 25, no. 8, pages 31 - 37 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112365175A (en) * | 2020-11-17 | 2021-02-12 | 杭州安恒信息安全技术有限公司 | Enterprise area-based economic analysis method, system, storage medium and equipment |
| CN112700083A (en) * | 2020-11-30 | 2021-04-23 | 北京中电普华信息技术有限公司 | Method and device for constructing scene of resource comprehensive utilization and service index system |
| CN112884347A (en) * | 2021-03-11 | 2021-06-01 | 清华大学 | Urban energy balance management system and construction method thereof |
| CN115809783A (en) * | 2022-12-09 | 2023-03-17 | 国网江苏省电力有限公司扬州供电分公司 | Method and device for evaluating and predicting industry-divided energy efficiency of medium-and-large-sized energy users |
| CN115809783B (en) * | 2022-12-09 | 2023-09-29 | 国网江苏省电力有限公司扬州供电分公司 | Energy efficiency evaluation prediction method for middle and large-sized energy users in different industries |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111861260A (en) | Regional energy economic operation and energy efficiency analysis method and terminal based on energy big data | |
| Liu et al. | Measuring polycentric urban development in China: An intercity transportation network perspective | |
| Sinha et al. | The multiple-choice knapsack problem | |
| AU2019253789B1 (en) | Distributed resource allocation | |
| WO2022142001A1 (en) | Target object evaluation method based on multi-score card fusion, and related device therefor | |
| CN107563845A (en) | A kind of on-line machining entrusting system and method | |
| CN113643103A (en) | Product recommendation method, device, equipment and storage medium based on user similarity | |
| CN105590281A (en) | System and method for education big data processing | |
| Frischmann | Infrastructure commons in economic perspective | |
| WO2022046590A1 (en) | Scheduling optimization | |
| Blech et al. | Efficient incident handling in industrial automation through collaborative engineering | |
| WO2016081628A1 (en) | System and method for aggregating and analyzing data and creating a spatial and/or non-spatial graphical display based on the aggregated data | |
| Knapp et al. | Alaskan stakeholder-defined research needs in the context of climate change | |
| Hinson et al. | The Internet and export: Some cross-country evidence from selected African countries | |
| Liu et al. | Development of a strategic value assessment model for smart city | |
| Srivastav et al. | Review on e-Learning Environment Development and context aware recommendation systems using Deep Learning | |
| Cheng et al. | Evaluation and analysis of regional economic growth factors in digital economy based on the deep neural network | |
| CN102420862A (en) | Internet of things middleware message description structure and form conversion method thereof | |
| US10748104B2 (en) | System and method of parallelizing order-by-order planning | |
| Amaral et al. | Digital divides across consumers of internet services in Spain using panel data 2007-2019. Narrowing or not? | |
| CN103729441B (en) | media object display method and device | |
| Lampropoulos | Artificial intelligence in smart grids: A bibliometric analysis and scientific mapping study | |
| Haider et al. | Impact of Online and Social Media Platforms in Organizing the Events: A Case Study on Coke Fest and Pakistan Super League | |
| Kushagra et al. | Cloud doctrine: impact on cloud adoption in the government organizations of India | |
| Wang | Research on basic information of enterprise electronization under the background of big data |
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 |