CN107832949A - A kind of big data method of servicing towards power consumer - Google Patents
A kind of big data method of servicing towards power consumer Download PDFInfo
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Abstract
The invention discloses a kind of big data method of servicing towards power consumer, step 1：Dsm/demand response；Step 2：User's Energy Efficiency Analysis and management；Step 3：Sales service assistant analysis；Step 4：Electric service public sentiment monitoring and warning is analyzed；Step 5：Charging electric vehicle Facilities Construction is disposed.A kind of big data method of servicing towards power consumer provided by the invention, it is huge for the value of data, not only the horizontal lifting of the management operating of power network itself can be arrived new height, even produce the change of essence, and more preferably services can be provided for government department, industrial quarters and users, expanding many value-added services for Utilities Electric Co. provides condition.
Description
Technical field
The present invention relates to a kind of big data method of servicing towards power consumer, belong to field of computer technology.
Background technology
Big data early stage is mainly used in the fields such as business, finance, gradually expands to the fields such as traffic, medical treatment, the energy afterwards,
Intelligent grid is counted as one of important technology field of big data application.
With the fast development of intelligent grid, a large amount of deployment of intelligent electric meter and the extensive use of sensing technology, electric power work
Industry generates the complicated data in a large amount of various structures, source, how to store and is the difficulty that Utilities Electric Co. faces using these data
Topic.
The content of the invention
Purpose：In order to overcome the deficiencies in the prior art, the present invention provides a kind of big data towards power consumer
Method of servicing.
Technical scheme：In order to solve the above technical problems, the technical solution adopted by the present invention is：
A kind of big data method of servicing towards power consumer, comprise the following steps：
Step 1：Dsm/demand response；
Step 2：User's Energy Efficiency Analysis and management；
Step 3：Sales service assistant analysis；
Step 4：Electric service public sentiment monitoring and warning is analyzed；
Step 5：Charging electric vehicle Facilities Construction is disposed.
Preferably, the step 1 includes：According to different weather conditions, including：Humidity, dryly band, temperature
High and low area, different social strata are classified user；The day of different electrical equipments can be drawn again for every a kind of user
Load curve, the use electrical characteristics of its main electrical equipment are analyzed, including：Power consumption occur time interval, power consumption influence because
Element, and it is whether transferable, whether can cut down；For the electrical equipment influenceed by weather, including：Water heater, air-conditioning, analysis are used
Electric equipment is to the sensitiveness of weather, and analysis different user is to the sensitiveness of electricity price, on the basis of classification analysis, by polymerization,
Obtain a certain panel region or certain a kind of available demand response total amount of user, then analyze which portion capacity, how long section
Demand response amount be it is reliable, analysis result can for formulate demand management/responsing excitation mechanism foundation be provided.
Preferably, the step 2 includes：User power utilization device classification electricity consumption data is collected, is obtained by sensor
After the data of different electrical appliances, Energy Efficiency Analysis conclusion is provided by being compared with typical data, average data；Pass through ammeter number
According to identifying the different type load proportion of user terminal, and Energy Efficiency Analysis result is drawn with the comparison of typical data；From mass users
Load curve, using data mining technology, according to specific function algorithm, born by the typical case for aggregating into industry in industry, season
Lotus curve model, then the typical load curve of the load curve of all users and industry is contrasted, analyzed and allusion quotation
The inconsistent user of type load curve variation tendency, evaluation thus is provided to the efficiency of user, and propose recommendation on improvement.
Preferably, the step 3 includes：Business Process System assistant analysis is integrated into tie to seek to match somebody with somebody, and will use telecommunications
Breath acquisition system, marketing system and the data of PMS and SCADA system blend, realize to transformer station, circuit and lower extension user and
The load of taiwan area, electric quantity monitoring analysis.
Preferably, the step 4 includes：By including with internet new media：The service docking of microblogging, wechat
Mechanism collects magnanimity power information, user profile and internet public opinion information, builds big data public sentiment monitoring analysis system, profit
Gathered, stored with big data, analyzed, digging technology, excavated from internet mass data, refine key message, established negative
Information correlation analysis monitoring model, see clearly in time and customer in response behavior, expansion internet marketing service channel, enterprise essence
Beneficial marketing management and good service are horizontal.
Preferably, the step 5 includes：Merge automobile user information, resident information, distribution network data,
Power information data, GIS data, socioeconomic data, grown using the brachymedial of big data technological prediction electric automobile
Phase recoverable amount, development scale and trend, electrical demand and peak load situation；With reference to traffic density, user's trip mode, charging
Mode preference factor, according to city and traffic programme and Transmission Expansion Planning in Electric, establish charging electric vehicle facilities planning model and
Assessment models afterwards, the measures of effectiveness for the deployment scheme of charging electric vehicle facility being formulated and being built the later stage provide foundation.
Beneficial effect：A kind of big data method of servicing towards power consumer provided by the invention, the utilization for data
Huge value, not only the horizontal lifting of the management operating of power network itself can be arrived new height, or even produce the change of essence, and
And more preferably services can be provided for government department, industrial quarters and users, expand many value-added services for Utilities Electric Co.
Offer condition.
Embodiment
A kind of big data method of servicing towards power consumer, comprise the following steps：
1）Dsm/demand response：According to different weather conditions (such as moist, dryly band, the high and low area of temperature),
Different social strata is classified user；The daily load curve of different electrical equipments can be drawn again for every a kind of user,
Analyze the use electrical characteristics of its main electrical equipment, include the time interval that power consumption occurs, power consumption influence factor, and whether
It is transferable, whether can cut down, for the electrical equipment influenceed by weather, such as water heater, air-conditioning, it need to be analyzed to weather
Sensitiveness, certainly, different seasons and it is daily in different time, user power utilization is all different to the sensitiveness of weather.
Different user is analyzed to the sensitiveness of electricity price, is included in Various Seasonal, different time to the sensitiveness of electricity price.In classification analysis
On the basis of, by polymerization, a certain panel region or certain a kind of available demand response total amount of user are can obtain, then which portion analyzed
Partial volume amount, how long the demand response amount of section is reliable, and analysis result can be to formulate demand management/responsing excitation mechanism
Foundation is provided.
2）User's Energy Efficiency Analysis and management：Power consumption efficiency analysis is carried out to user, it is necessary first to collect user power utilization device
Classification electricity consumption data.Before intelligent electric meter deployment, intrusive mood method is used, for example, at different electrical equipment wiring more
Install sensor additional., can be by being compared with typical data, average data after the data that different electrical appliances are obtained by sensor
Provide Energy Efficiency Analysis conclusion.In the case where intelligent electric meter is largely disposed, because intelligent electric meter can obtain short period of time
Electricity consumption data, without installing sensor additional again, the different type load proportion of user terminal by ammeter data, can be identified, and
Comparison with typical data draws Energy Efficiency Analysis result.From the load curve of mass users, using data mining technology, according to spy
Fixed function algorithm, it is then that the load of all users is bent by the typical load curve model for aggregating into industry in industry, season
Line and the typical load curve of industry are contrasted, and analyze the user inconsistent with typical load curve variation tendency, thus
Evaluation is provided to the efficiency of user, and proposes recommendation on improvement.
3）The sales service assistant analysis such as Business Process System：Business Process System assistant analysis is integrated into tie to seek to match somebody with somebody, by electricity consumption
The data of information acquisition system, marketing system and PMS and SCADA system blend, and realize to transformer station, circuit and lower extension user
Load, electric quantity monitoring analysis with taiwan area, technical support is provided to accelerate the speed of Business Process System and improving electric service level.
Grid equipment reliability of operation is greatly enhanced simultaneously, to optimize distribution net work structure, power network fault in production is reduced, improves company
Power marketing management lean level provides means.
4）Electric service public sentiment monitoring and warning is analyzed：Pass through the service docking machine with the internet new media such as microblogging, wechat
System collects magnanimity power information, user profile and internet public opinion information, builds big data public sentiment monitoring analysis system, utilizes
Big data collection, storage, analysis, digging technology, excavated from internet mass data, refine key message, establish negative letter
Association analysis monitoring model is ceased, is seen clearly in time and customer in response behavior, expansion internet marketing service channel, enterprise lean
Marketing management and good service are horizontal.
5）Charging electric vehicle Facilities Construction is disposed：Merge automobile user information, resident information, distribution network data,
Power information data, GIS data, socioeconomic data etc., using the short of big data technological prediction electric automobile
Situations such as medium-term and long-term recoverable amount, development scale and trend, electrical demand and peak load.With reference to traffic density, user trip side
The factors such as formula, charging modes preference, according to city and traffic programme and Transmission Expansion Planning in Electric, establish charging electric vehicle facility rule
Draw model and rear assessment models, the deployment scheme of charging electric vehicle facility is formulated and built the later stage measures of effectiveness provide according to
According to.
Described above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (6)
- A kind of 1. big data method of servicing towards power consumer, it is characterised in that：Comprise the following steps：Step 1：Dsm/demand response；Step 2：User's Energy Efficiency Analysis and management；Step 3：Sales service assistant analysis；Step 4：Electric service public sentiment monitoring and warning is analyzed；Step 5：Charging electric vehicle Facilities Construction is disposed.
- A kind of 2. big data method of servicing towards power consumer according to claim 1, it is characterised in that：The step 1 includes：According to different weather conditions, including：Humidity, dryly band, the high and low area of temperature, different social strata will be used Classified at family；The daily load curve of different electrical equipments can be drawn again for every a kind of user, analyze its main electrical equipment Use electrical characteristics, including：Power consumption occur time interval, power consumption influence factor, and it is whether transferable, whether can cut Subtract；For the electrical equipment influenceed by weather, including：Water heater, air-conditioning, analysis electrical equipment is to the sensitiveness of weather, analysis Different user on the basis of classification analysis, by polymerization, obtains a certain panel region or certain a kind of user to the sensitiveness of electricity price Available demand response total amount, then analyze which portion capacity, how long the demand response amount of section be it is reliable, analysis knot Fruit can provide foundation to formulate demand management/responsing excitation mechanism.
- A kind of 3. big data method of servicing towards power consumer according to claim 1, it is characterised in that：The step 2 include：Collect user power utilization device classification electricity consumption data, after the data that different electrical appliances are obtained by sensor, by with typical case Data, average data, which are compared, provides Energy Efficiency Analysis conclusion；By ammeter data, the different type duty ratio of user terminal is identified Example, and draw Energy Efficiency Analysis result with the comparison of typical data；From the load curve of mass users, using data mining technology, According to specific function algorithm, by the typical load curve model for aggregating into industry in industry, season, then by all users' Load curve and the typical load curve of industry are contrasted, and analyze the use inconsistent with typical load curve variation tendency Family, evaluation thus is provided to the efficiency of user, and propose recommendation on improvement.
- A kind of 4. big data method of servicing towards power consumer according to claim 1, it is characterised in that：The step 3 include：Business Process System assistant analysis is integrated into tie to seek to match somebody with somebody, by power information acquisition system, marketing system and PMS and The data of SCADA system blend, and realize to transformer station, circuit and the lower load for hanging user and taiwan area, electric quantity monitoring analysis.
- A kind of 5. big data method of servicing towards power consumer according to claim 1, it is characterised in that：The step 4 include：By including with internet new media：Microblogging, the service berthing mechanism of wechat collect magnanimity power information, user profile And internet public opinion information, big data public sentiment monitoring analysis system is built, is gathered, stored using big data, analyzed, Mining Technology Art, excavated from internet mass data, refine key message, established negative report association analysis monitoring model, see clearly in time With customer in response behavior, internet marketing service channel is expanded, the marketing management of enterprise lean and good service are horizontal.
- A kind of 6. big data method of servicing towards power consumer according to claim 1, it is characterised in that：The step 5 include：Merge automobile user information, resident information, distribution network data, power information data, GIS data, Socioeconomic data, needed using the long-term recoverable amount of brachymedial, development scale and the trend of big data technological prediction electric automobile, electricity Summation peak load situation；With reference to traffic density, user's trip mode, charging modes preference factor, advised according to city and traffic Draw and Transmission Expansion Planning in Electric, charging electric vehicle facilities planning model and rear assessment models are established, to charging electric vehicle facility Deployment scheme formulate and build the later stage measures of effectiveness provide foundation.
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Cited By (5)
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| CN109471381A (en) * | 2018-09-12 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | Energy efficiency of equipment integrated control method based on big data fusion |
| CN109829608A (en) * | 2018-12-19 | 2019-05-31 | 国网山西省电力公司长治供电公司 | A kind of service providing method based on user's energy consumption related data |
| CN110019173A (en) * | 2018-09-12 | 2019-07-16 | 国网浙江省电力有限公司嘉兴供电公司 | The energy efficiency of equipment control method of big data |
| CN110188255A (en) * | 2018-12-19 | 2019-08-30 | 国网福建省电力有限公司 | Power consumer Behavior mining method and system based on the shared fusion of business datum |
| WO2022041265A1 (en) * | 2020-08-31 | 2022-03-03 | 苏州大成电子科技有限公司 | Big data service method for electric vehicle power user |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109471381A (en) * | 2018-09-12 | 2019-03-15 | 国网浙江省电力有限公司嘉兴供电公司 | Energy efficiency of equipment integrated control method based on big data fusion |
| CN110019173A (en) * | 2018-09-12 | 2019-07-16 | 国网浙江省电力有限公司嘉兴供电公司 | The energy efficiency of equipment control method of big data |
| CN110019173B (en) * | 2018-09-12 | 2023-05-05 | 国网浙江省电力有限公司嘉兴供电公司 | Equipment energy efficiency control method for big data |
| CN109829608A (en) * | 2018-12-19 | 2019-05-31 | 国网山西省电力公司长治供电公司 | A kind of service providing method based on user's energy consumption related data |
| CN110188255A (en) * | 2018-12-19 | 2019-08-30 | 国网福建省电力有限公司 | Power consumer Behavior mining method and system based on the shared fusion of business datum |
| CN110188255B (en) * | 2018-12-19 | 2021-06-01 | 国网福建省电力有限公司 | Power consumer behavior mining method and system based on business data sharing fusion |
| WO2022041265A1 (en) * | 2020-08-31 | 2022-03-03 | 苏州大成电子科技有限公司 | Big data service method for electric vehicle power user |
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