CN108256783A - Energy consumption index dynamic allocation method and system based on data-driven model Demand-Oriented - Google Patents
Energy consumption index dynamic allocation method and system based on data-driven model Demand-Oriented Download PDFInfo
- Publication number
- CN108256783A CN108256783A CN201810129385.XA CN201810129385A CN108256783A CN 108256783 A CN108256783 A CN 108256783A CN 201810129385 A CN201810129385 A CN 201810129385A CN 108256783 A CN108256783 A CN 108256783A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- model
- energy
- building
- region
- 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
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
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
- G06Q50/08—Construction
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a kind of energy consumption index dynamic allocation methods and system based on data-driven model Demand-Oriented, the constraint of total energy consumption limit and each region basal energy expenditure constraint of demand are built by consideration, build the unit interval energy consumption index static distribution model in each region；Unit interval is carried out to the division at time point, considers the request distribution lowest energy consumption value at each region each time point and energy consumption limit, in the real-time dynamic allocation for dividing time point progress energy consumption, formation is preliminary to dynamically distribute model；Solution is optimized to tentatively dynamically distributing model, using buildings model and regional model data, carries out the optimization computation of energy consumption index, obtains the Energy dissipation scheme in each region.It can realize and more accurately carry out Energy dissipation.
Description
Technical field
The present invention relates to a kind of energy consumption index dynamic allocation methods and system based on data-driven model Demand-Oriented.
Background technology
Building accounts for the 40% of global energy consumption, accounts for the 30% of total CO 2 discharge capacity, monitors building energy consumption and closes
Reason formulates building energy plan, realizes building energy scheduling, can effectively reduce building energy consumption greenhouse related to building gas
Body discharges, and is one of the developing direction in building energy saving field future.In recent years, intelligent grid demand side management is more and more important,
Intelligent grid emphasizes that information and two-way circulating for electric energy participate in operation of power networks, and according to Spot Price tune with interaction, encouragement user
It is whole to use power mode.Building determines the energy consumption index of building, and will feed back to intelligence with energy information as one of electric power terminal user
Power grid plays an important roll the healthy O＆M of intelligent grid.
For the energy scheduling problem of building, currently ground on the building energy consumption Allocation method of Demand-Oriented both at home and abroad
Study carefully less, most of research concentrates on energy consumption monitoring and static prediction, and the energy consumption historical data of Main Basiss building O＆M is ground
Study carefully the energy consumption static prediction in building concentration or region, deficiency is considered to the dynamic need of energy consumption, how to realize Demand-Oriented
One of research hotspot that the dynamic allocation of building energy consumption index are newly risen in recent years.
Invention content
The present invention is to solve the above-mentioned problems, it is proposed that a kind of energy consumption index based on data-driven model Demand-Oriented moves
State distribution method and system, the present invention are daily by building each region (such as room separate space) under building energy consumption restrictive condition
The static distribution model of energy consumption index, and then consider the real time problems of Energy dissipation, Energy dissipation dynamic model is established, is realized
The energy consumption in each region dynamically distributes, carry out Energy dissipation that can be more scientific, energy saving resource.
To achieve these goals, the present invention adopts the following technical scheme that：
A kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented, includes the following steps：
(1) constraint of building total energy consumption limit and each region basal energy expenditure constraint of demand are considered, when building the unit in each region
Between energy consumption index static distribution model；
(2) unit interval is carried out to the division at time point, considers the request distribution lowest energy consumption value at each region each time point
With energy consumption limit, in the real-time dynamic allocation for dividing time point progress energy consumption, preliminary dynamic allocation model is formed；
(3) solution is optimized to tentatively dynamically distributing model, using buildings model and regional model data, carries out energy consumption
The optimization computation of index obtains the Energy dissipation scheme in each region.
Further, the unit interval energy consumption index static distribution model in each region is the energy consumption of each region distribution and its
The sum of the ratio of area.
Further, building total energy consumption limit is constrained to building total energy consumption and should be less than rejecting within the unit interval equal to building
Limit value other than required energy consumption.
Further, each region basal energy expenditure constraint of demand need to meet normal work and base by the energy consumption that each region is distributed
Energy requirements in the case of this comfort requirement, the energy consumption that each region is distributed need to meet normal work and basic comfort requirement situation
Under energy requirements excavate to obtain using big data is built.
Further, the constraint of energy consumption index static distribution model further includes《Building energy consumption standard》To different kinds of building
The unit area energy consumption index binding occurrence that specific energy consumption proposes.
Further, the constraint of energy consumption index static distribution model further includes history energy consumption record, and history energy consumption is recorded
In the maximum reference value of the energy consumption maximum value excavated as Energy dissipation.
Further, it when some region since the energy consumption index use that additional task is distributed finishes, needs further
With can when, it will send out requirement request, whether at the time point of distribution when such event occurs, will all carry out energy consumption index
Real-Time Scheduling, the lowest energy consumption value of distribution is asked to consider by the region time point pervious power consumption values and at the time point
Into the constraint of dynamic distribution model.
Further, the model of structure is solved using simplex method linear programming.
A kind of energy consumption index dynamic allocation system based on data-driven model Demand-Oriented, including database, energy consumption point
With algoritic module and energy consumption Real-Time Scheduling module, wherein：
The database is configured as storage building attribute information, area attribute information and the basal energy expenditure letter in each region
Breath；
The Energy dissipation algoritic module is configured as considering that the constraint of building total energy consumption limit and each region basal energy expenditure need
Constraint is asked, builds the unit interval energy consumption index static distribution model in each region, the unit interval is carried out to the division at time point, is examined
The request distribution lowest energy consumption value at each region each time point and energy consumption limit are considered, in the real-time dynamic for dividing time point progress energy consumption
Distribution forms preliminary dynamic allocation model；
The energy consumption Real-Time Scheduling module is configured as the data using buildings model and regional model, carries out energy consumption point
Optimization computation with index, exports the Energy dissipation numerical value in each room, and and carries out the Real-Time Scheduling of energy consumption.
Further, the Energy dissipation algoritic module includes data acquisition module, building and regional model module and most
Optimization algorithm module, wherein：
The data acquisition module is configured as obtaining the attribute information of the building by building ID, be obtained by building ID
Take all node IDs of the building and its essential information, the energy consumption limitation letter by obtaining each region corresponding to node ID information
Breath；
Building and regional model module, are configured as structure buildings model and each region mould positioned at the building interior
Type, buildings model provide the interface of parameters acquisition with each region model；
Optimization algorithm module, the constraint for being configured as being stored in Energy dissipation algorithm and the specific implementation letter of optimization
Number using buildings model and the data of room model, carries out the optimization computation of Energy dissipation index, exports the energy in each room
Consumption distribution numerical value.
The node refers to Internet of things node, has the function of data forwarding, area equipment management etc..
Compared with prior art, beneficial effects of the present invention are：
1st, during present invention structure dynamic need model, passing historical data, Neng Gougeng is utilized in comprehensive energy consumption static prediction
Accurately carry out energy consumption prediction and distribution；
2nd, the present invention in combination with Internet of Things technology, accurately holds the dynamic energy consumption demand of each region, is provided for intelligent grid
On the basis of more accurate user side demand, the energy scheduling of intelligent grid may participate in；Contribute to the health fortune of intelligent grid
Dimension and scheduling.
3rd, the present invention is when build dynamic need model, consider whole energy consumption limit, fire-fighting must limit etc. it is regional or build
Restrictive condition itself is built, there is stronger practicability.
Description of the drawings
The accompanying drawings which form a part of this application are used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation do not form the improper restriction to the application for explaining the application.
Fig. 1 is dynamic allocation method flow chart.
Specific embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.It is unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or combination thereof.
In the present invention, term as " on ", " under ", "left", "right", "front", "rear", " vertical ", " level ", " side ",
The orientation or position relationship of instructions such as " bottoms " are based on orientation shown in the drawings or position relationship, only to facilitate describing this hair
Bright each component or component structure relationship and determining relative, not refer in particular to either component or element in the present invention, it is impossible to understand
For limitation of the present invention.
In the present invention, term such as " affixed ", " connected ", " connection " should be interpreted broadly, and expression can be fixedly connected,
Can also be integrally connected or be detachably connected；It can be directly connected, can also be indirectly connected by intermediary.For
The related scientific research of this field or technical staff can determine the concrete meaning of above-mentioned term in the present invention as the case may be,
It is not considered as limiting the invention.
As shown in Figure 1, the present invention is to achieve the above object, it is proposed to adopt the following technical solutions.Include multiple sections in building
Point, each region at least have there are one node, and node refers to Internet of things node, has the work(such as data forwarding, area equipment management
Can, it is the prior art, details are not described herein.
The realization technical solution of static energy consumption distribution model is as follows：
Building is built by the principle of the static distribution model of the daily energy consumption index in room each under building energy consumption restrictive condition
Static Energy dissipation software module.
The module is made of three parts, database, static energy consumption allocation algorithm mould of the storage building with room details
Block, energy consumption Real-Time Scheduling module.
Database part is made of two tables, including building attribute list, room property table.Attribute information is clearly built, and
It is stored into database, building attribute information table is as follows：
The attribute information of room/region clearly in building, and be entered into database, by room property table come pipe
Reason, it is as follows：
Energy consumption index static distribution model software module is by data acquisition module, building and room model module, optimization
Algoritic module three parts are formed.
Data acquisition module includes：The attribute information of the building is obtained by building ID, obtains the building by building ID
All node IDs and its essential information, the energy consumption restricted information by obtaining each room/region corresponding to node ID information.
Buildings model module includes building model and room model, when being calculated, calculates granularity as single seat building
The Energy dissipation plan in each room, one build in building only to need to operate there are one the example of buildings model in itself, and
Room management then needs the example of multiple room models.Buildings model provides the interface of parameters acquisition with room model.
Energy dissipation optimization algorithm module, constraint in Energy dissipation algorithm is encapsulated in algoritic module and is optimized
Function is implemented, Energy dissipation optimization algorithm is as follows：
Wherein：eiThe energy consumption of room distribution where node i；
SiThe area in room where node i；
L is to build the limit rejected for certain day other than the required energy consumption such as fire-fighting instruction；
AndThe energy consumption in the case of normal work and basic comfort requirement need to be met by the energy consumption that each room distributes
Demand；
EmaxFor the unit area energy consumption index binding occurrence required by standard；
For room energy consumption maximum value.
In calculating process, using buildings model and the data of room model instance, carry out energy consumption index static allocation and refer to
Target optimization computation exports the Energy dissipation numerical value in each room.
Energy consumption scheduler module communicates with energy resource system, according to allocated energy consumption index value, realizes the energy consumption in each room
Distribution Indexes.
It is as follows to implement step：
Step 1：Input building ID, by data acquisition module obtain building ID corresponding to building entirety energy consumption limit,
Fire-fighting must limit, unit area limit and instantiate buildings model；
Step 2：All node IDs and its corresponding room face amount, each power consumption constraint value in inquiry building ID, example
Change room model, and using room ID as each room example of key value O＆Ms；
Step 3：Optimization algorithm module is instantiated, using the data in buildings model example and room model instance, into
Row energy consumption Optimal calculation, and export the energy consumption index apportioning cost in each room.
Step 4：Energy consumption Real-Time Scheduling module communicates with energy resource system, according to allocated energy consumption index value, realizes each
The energy consumption index distribution in room.
Dynamic energy consumption Allocation method considers real-time, is improved on the basis of Static State Index distribution method, tool
Body realizes that process is as follows：
Building is built by the principle of the dynamic allocation model of the daily energy consumption index in room each under building energy consumption restrictive condition
Static Energy dissipation software module.
Daily 24 hours are divided, in the real-time dynamic allocation for dividing time point progress energy consumption.In addition to this, when certain
A little rooms are since the energy consumption index use that additional task is distributed finishes, when needing further with energy, it will sending out demand please
It asks, the Real-Time Scheduling that such event whether will all carry out energy consumption index when occurring at the time point of distribution, system.
The module is made of four parts, and the database of storage building and room details, energy consumption real time data calculate
Module, dynamic energy consumption allocation algorithm module, energy consumption Real-Time Scheduling module.
Database part is made of four tables, including building attribute information table, building real time energy consumption statistics table, room
Essential attribute table, room real time energy consumption statistics table.Essential attribute information is clearly built, and is stored into database, is built
Attribute information table is as follows：
The energy consumption real time energy consumption information table of building is as follows：
| Build ID | Time | Building has consumed power consumption values |
The attribute information of room/region clearly in building, and be entered into database, pass through two room essential attributes
Table is managed with energy consumption attribute list, as follows：
The not real time energy consumption table of chummery, it is as follows：
The real-time computing module of energy consumption calculates the energy that the real time energy consumption of building, each room have consumed at defined time point
Consumption value, the real time energy consumption value of room current time normal work demand, currently expires at the lowest energy consumption value that room is asked under the moment
Real time energy consumption value, the maximum value of history energy consumption of the substantially true demand of foot.Before dynamic energy consumption distribution is carried out, it is real-time to carry out energy consumption
It calculates, energy consumption is packaged with the calculating of the Various types of data such as big data analysis, data mining and parser in calculating in real time, realized each
The calculating of item real time energy consumption.
Energy consumption dynamic distribution model software module is by data acquisition module, building and room model module, optimization
Algoritic module three parts are formed.
Data acquisition module includes：The attribute information of the building is obtained by building ID, obtains building energy by building ID
The real time information of consumption, by build ID obtain the building all node IDs and its essential information, obtained by node ID it is each
The real time energy consumption information of room/region.
Buildings model module includes building model and room model, when being calculated, calculates granularity as single seat building
The Energy dissipation plan in each room, one build in building only to need to operate there are one the example of buildings model in itself, and
Room management then needs the example of multiple room models.Buildings model provides the interface of parameters acquisition with room model.
Dynamic energy consumption allocation optimization algoritic module, encapsulate in algoritic module constraint in Energy dissipation algorithm with it is optimal
The specific implementation function of change, Energy dissipation optimization algorithm are as follows：
It is assumed that distribution task is happened at moment t, the energy consumption index of each room this moment to this end of day is
WhereinFor the building moment pervious power consumption values of t,AndTo meet normal work and base after moment t
Energy requirements in the case of this comfort requirement are intended excavating to obtain according to building big data,For the pervious energy consumptions of room i moment t
Value,The lowest energy consumption value distributed for room i in moment t requests.
In calculating process, using buildings model and the data of room model instance, carry out energy consumption index dynamic allocation and refer to
Target optimization computation, the energy consumption for exporting each room distribute numerical value in real time.
Energy consumption Real-Time Scheduling module communicates with energy resource system, according to allocated dynamic energy consumption index value, realizes each room
Between energy consumption index distribution, while receive each room dynamic energy consumption request, to energy resource system ask Energy dissipation.
It is as follows to implement step：
Step 1：Calculate the real time energy consumption of building, each room have consumed at defined time point power consumption values, this when
The lowest energy consumption value of room request, the real time energy consumption value of room current time normal work demand, current satisfaction is inscribed substantially to belong to
The real time energy consumption value of real demand, the maximum value of history energy consumption, and be stored into database building real time energy consumption table and room it is real-time
In energy consumption table.
Step 1：Input building ID, by data acquisition module obtain building ID corresponding to building entirety energy consumption limit,
Fire-fighting must limit, unit area limit, consumed energy consumption real time data and instantiated buildings model；
Step 2：The energy consumption that all node IDs and its corresponding room face amount, each room have consumed in inquiry building ID
The lowest energy consumption value of room request, the real time energy consumption value of room current time normal work demand, current satisfaction under value, the moment
Substantially the real time energy consumption value of true demand, the maximum value of history energy consumption instantiate room model, and are transported by KEY values of room ID
Tie up each room example；
Step 3：Optimization algorithm module is instantiated, using the data in buildings model example and room model instance, into
Mobile state energy consumption Optimal calculation, and export the real time energy consumption Distribution Indexes value in each room.
Step 4：Energy consumption Real-Time Scheduling module distributes the energy consumption in each room according to allocated dynamic energy consumption index value
Index, while receive the dynamic energy consumption request in each room, distribution request energy consumption.
The foregoing is merely the preferred embodiments of the application, are not limited to the application, for the skill of this field
For art personnel, the application can have various modifications and variations.It is all within spirit herein and principle, made any repair
Change, equivalent replacement, improvement etc., should be included within the protection domain of the application.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (10)
1. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented, it is characterized in that：Including following step
Suddenly：
(1) consider the constraint of building total energy consumption limit and each region basal energy expenditure constraint of demand, build the unit interval energy in each region
Consume index static distribution model；
(2) unit interval is carried out to the division at time point, considers the request distribution lowest energy consumption value and energy at each region each time point
Limit is consumed, in the real-time dynamic allocation for dividing time point progress energy consumption, is formed and tentatively dynamically distributes model；
(3) solution is optimized to tentatively dynamically distributing model, using buildings model and regional model data, carries out energy consumption index
Optimization computation, obtain the Energy dissipation scheme in each region.
2. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：The unit interval energy consumption index static distribution model in each region is the energy consumption of each region distribution and the ratio of its area
Sum.
3. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：Building total energy consumption limit be constrained to building total energy consumption should be less than being equal to building rejected within the unit interval required energy consumption with
Outer limit value.
4. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：Each region basal energy expenditure constraint of demand need to meet normal work and basic comfort requirement by the energy consumption that each region is distributed
In the case of energy requirements, the energy consumption that each region is distributed need to meet the energy consumption need in the case of normal work and basic comfort requirement
It asks and excavates to obtain using building big data.
5. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：The constraint of energy consumption index static distribution model further includes《Building energy consumption standard》Different kinds of building specific energy consumption is carried
The unit area energy consumption index binding occurrence gone out.
6. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：The constraint of energy consumption index static distribution model further includes history energy consumption record, is excavated during history energy consumption is recorded
Maximum reference value of the energy consumption maximum value as Energy dissipation.
7. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：When some region since the energy consumption index use that additional task is distributed finishes, when needing further with energy, it will
Send out requirement request, whether at the time point of distribution when such event occurs, the Real-Time Scheduling that will all carry out energy consumption index,
The lowest energy consumption value of distribution is asked to take into account dynamic allocation by the region time point pervious power consumption values and at the time point
The constraint of model.
8. a kind of energy consumption index dynamic allocation method based on data-driven model Demand-Oriented as described in claim 1,
It is characterized in：The model of structure is solved using simplex method linear programming.
9. a kind of energy consumption index dynamic allocation system based on data-driven model Demand-Oriented, it is characterized in that：Including database,
Energy dissipation algoritic module and energy consumption Real-Time Scheduling module, wherein：
The database is configured as storage building attribute information, area attribute information and the basal energy expenditure information in each region；
The Energy dissipation algoritic module is configured as considering the constraint of building total energy consumption limit and each region basal energy expenditure demand about
Beam builds the unit interval energy consumption index static distribution model in each region, and the unit interval is carried out to the division at time point, considers each
The request distribution lowest energy consumption value and energy consumption limit at region each time point, in the real-time dynamic point for dividing time point progress energy consumption
Match, form preliminary dynamic allocation model；
The energy consumption Real-Time Scheduling module is configured as the data using buildings model and regional model, carries out Energy dissipation and refers to
Target optimization computation, exports the Energy dissipation numerical value in each room, and and carries out the Real-Time Scheduling of energy consumption.
10. a kind of energy consumption index dynamic allocation system based on data-driven model Demand-Oriented as described in claim 9,
It is characterized in that：The Energy dissipation algoritic module includes data acquisition module, building and regional model module and optimization algorithm
Module, wherein：
The data acquisition module is configured as obtaining the attribute information of the building by building ID, is somebody's turn to do by building ID acquisitions
All node IDs and its essential information of building, the energy consumption restricted information by obtaining each region corresponding to node ID information；
Building and regional model module, are configured as structure buildings model and each region model positioned at the building interior, build
Build the interface that model provides parameters acquisition with each region model；
Optimization algorithm module, the constraint for being configured as being stored in Energy dissipation algorithm and the specific implementation function of optimization,
Using buildings model and the data of room model, the optimization computation of Energy dissipation index is carried out, exports the energy consumption in each room
Distribute numerical value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810129385.XA CN108256783A (en) | 2018-02-08 | 2018-02-08 | Energy consumption index dynamic allocation method and system based on data-driven model Demand-Oriented |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810129385.XA CN108256783A (en) | 2018-02-08 | 2018-02-08 | Energy consumption index dynamic allocation method and system based on data-driven model Demand-Oriented |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108256783A true CN108256783A (en) | 2018-07-06 |
Family
ID=62744778
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810129385.XA Pending CN108256783A (en) | 2018-02-08 | 2018-02-08 | Energy consumption index dynamic allocation method and system based on data-driven model Demand-Oriented |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108256783A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104484715A (en) * | 2014-11-28 | 2015-04-01 | 江苏大学 | Neural network and particle swarm optimization algorithm-based building energy consumption predicting method |
| CN105446139A (en) * | 2015-12-18 | 2016-03-30 | 华南理工大学 | Construction energy consumption analysis method and construction energy consumption analysis system based on BP neural network |
| US20160359330A1 (en) * | 2015-06-06 | 2016-12-08 | Ruxiang Jin | Systems and Methods for Dynamic Energy Distribution |
| KR101683401B1 (en) * | 2015-11-24 | 2016-12-21 | 연세대학교 산학협력단 | Apparatus and method for evaluating energy performance of existing buildings using dynamic energy performance curve |
| CN106845701A (en) * | 2017-01-11 | 2017-06-13 | 东南大学 | A kind of integrated energy system optimization method based on heat supply network and house thermal inertia |
| CN106874581A (en) * | 2016-12-30 | 2017-06-20 | 浙江大学 | A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model |
| CN107102602A (en) * | 2017-06-21 | 2017-08-29 | 山东优筑智能科技股份有限公司 | One kind building building energy management system and method |
-
2018
- 2018-02-08 CN CN201810129385.XA patent/CN108256783A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104484715A (en) * | 2014-11-28 | 2015-04-01 | 江苏大学 | Neural network and particle swarm optimization algorithm-based building energy consumption predicting method |
| US20160359330A1 (en) * | 2015-06-06 | 2016-12-08 | Ruxiang Jin | Systems and Methods for Dynamic Energy Distribution |
| KR101683401B1 (en) * | 2015-11-24 | 2016-12-21 | 연세대학교 산학협력단 | Apparatus and method for evaluating energy performance of existing buildings using dynamic energy performance curve |
| CN105446139A (en) * | 2015-12-18 | 2016-03-30 | 华南理工大学 | Construction energy consumption analysis method and construction energy consumption analysis system based on BP neural network |
| CN106874581A (en) * | 2016-12-30 | 2017-06-20 | 浙江大学 | A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model |
| CN106845701A (en) * | 2017-01-11 | 2017-06-13 | 东南大学 | A kind of integrated energy system optimization method based on heat supply network and house thermal inertia |
| CN107102602A (en) * | 2017-06-21 | 2017-08-29 | 山东优筑智能科技股份有限公司 | One kind building building energy management system and method |
Non-Patent Citations (2)
| Title |
|---|
| CHENGDONG LI ET: "Building Energy Consumption Prediction：An Extreme Deep Learning Approach", 《ENERGIES》 * |
| 赵超等: "基于ＫＰＣＡ－ＷＬＳＳＶＭ的建筑能耗预测模型", 《土木建筑与环境工程》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103559656B (en) | A kind of demand response control system and method | |
| CN102074954A (en) | Comprehensive energy-conservation evaluation and decision-making method for urban and rural distribution network | |
| CN110111214B (en) | User energy management method and system based on priority | |
| CN109873421A (en) | A kind of provincial power network abundant intensity online evaluation method and system | |
| CN114243709A (en) | Scheduling operation method capable of adjusting resource layering and grading at demand side | |
| CN106849061A (en) | A kind of customer charge responsing excitation integral and system | |
| CN108364238A (en) | A kind of diversified powering mode selection method based on power supply area grade classification | |
| CN101916313A (en) | Function optimization method for city water-supply pipe network | |
| CN107169655A (en) | A kind of method and device of preferred power distribution network project compatibility | |
| CN113469585A (en) | Carbon management data adjusting method, carbon management platform, system and storage medium | |
| CN115564197A (en) | User side flexible resource response potential evaluation method, system, device and medium | |
| CN110335075A (en) | Intelligent marketing system and its working method suitable for the consumer finance | |
| CN113361980A (en) | Big data asset value evaluation system and method | |
| DE112014006204T5 (en) | Performance Exchange Management System and Performance Exchange Management Procedures | |
| CN106873552B (en) | The electric car charging monitoring system and method for group's intarconnected cotrol | |
| CN110061489B (en) | Control system of direct current microgrid | |
| CN108959746B (en) | Regional agricultural water consumption verification and decomposition method | |
| CN107491866B (en) | Provincial and regional integrated power grid safety and stability comprehensive defense method | |
| CN108256783A (en) | Energy consumption index dynamic allocation method and system based on data-driven model Demand-Oriented | |
| CN114079285A (en) | Power grid dispatching optimization system and method based on multiple time scales | |
| CN116307940A (en) | Enterprise operation management system and method | |
| CN107944687B (en) | Incremental power distribution network comprehensive evaluation method for power grid company insurance service | |
| CN111178658B (en) | Planned water use management method and system based on big data analysis | |
| Dunscombe et al. | Radiotherapy service delivery models for a dispersed patient population | |
| CN109034595A (en) | One kind being suitable for 10kV and following power distribution network investment quantization analysis decision Method and kit for |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180706 |
|
| RJ01 | Rejection of invention patent application after publication |