CN105811402A - Electric power load forecasting system and forecasting method - Google Patents
Electric power load forecasting system and forecasting method Download PDFInfo
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- CN105811402A CN105811402A CN201610163622.5A CN201610163622A CN105811402A CN 105811402 A CN105811402 A CN 105811402A CN 201610163622 A CN201610163622 A CN 201610163622A CN 105811402 A CN105811402 A CN 105811402A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0073—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source when the main path fails, e.g. transformers, busbars
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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Abstract
The invention belongs to the technical field of electric energy data processing of electric power systems, and particularly relates to an electric power load forecasting system and forecasting method. The electric power load forecasting system comprises an electric power load control unit, an electric energy collection unit, a power distribution network automatic service unit, an electric power marketing MIS service unit, a transformer substation protection center unit and an electric power dispatching center service unit, wherein the electric power load control unit is connected with the electric energy collection unit, the power distribution network automatic service unit, the electric power marketing MIS service unit, the transformer substation protection center unit and the electric power dispatching center service unit separately. The forecasting method is carried out by the steps of collecting information of electric power users by taking the names of power network lines as units, tracking and inquiring the information, carrying out calculation and analysis on the line load to obtain short-term load forecasting curves of each line of the power distribution network, and various forecasting indexes affecting the electric power load. According to the electric power load forecasting system and forecasting method, the reasons causing electric power load fluctuations can be tracked, and the external factors and influences can be evaluated and analyzed, so that monitoring and forecasting for electricity consumption can be realized.
Description
Technical field
The invention belongs to power system electric energy data analysis technical field, particularly relate to a kind of Electric Load Prediction System and Forecasting Methodology thereof.
Background technology
Substantial amounts of marketing, metering, operation of power networks data separate degree are not high.Development along with computer information technology, all kinds of operation systems (Marketing Management Information System, metering automation system, scheduling EMS system) have accumulated substantial amounts of data resource, system can gather and store a large amount of client's information about power, including client properties, client's load characteristic, electricity feature etc., cannot effectively be applied, be caused the waste of data resource.The Electricity market analysis become more meticulous is an extremely complex system engineering, and involved data volume is very huge, and the intelligent means of Electricity market analysis are not enough, and anthropic factor impact is more.Only cannot obtain effective conclusion with Market Analyst by simple calculating, and its subjectivity will have influence on the accuracy of the market analysis to a certain extent.If owing to post personnel variation, due to the deficiency of laggard employee experience accumulation in a short time, that can cause the reasonable science of forecast analysis can not be continuous.The economic situation of our times complexity increases the judgement difficulty to Power Market Development rule to a great extent, the change an urgent demand Utilities Electric Co. of economic situation understands the endogenous development change mechanism of power sales in depth, and seek its influence factor and regularity, provide scientific basis for scientific forecasting market future trend.
Current Electrical Market Forecasting analysis side overweights overall macroscopic analysis, lacks the analysis to microcosmic market.New change is constantly produced along with market form, need to improve the degree that becomes more meticulous of the market analysis, the refinement analytic function in the microcosmic markets such as market internal and external reasons analysis of Influential Factors, demand response, electrity market activity tracking, monitoring is provided, market analysis forecast work often relies on power system internal data, have ignored the trace analysis to external environmental effect.The development of electricity market is subject to the impact of multiple external environment, and particularly with the bigger region of industry proportion, economical whether boom affects Power Market Development running orbit to a great extent.In the past for the analysis of external economy environment often based on postmortem analysis, future electrical energy cannot be instructed to develop, therefore, grid company can clearly cause the major influence factors of turn of the market, therefrom excavate and affect the main industries of electricity market change, main users and reason, contacting between accurate assurance user, the fluctuation of industry quantity of electricity and area quantity of electricity fluctuation, considering its impact on electrical network for user provides decision-making foundation.
Summary of the invention
The purpose of the present invention is the problems referred to above solving prior art, a kind of Electric Load Prediction System and Forecasting Methodology thereof are provided, the present invention electric load can be carried out Real-time Collection tracking and power structure solution is analyzed, judge that electric load fluctuation is monitored and statistical analysis, realize the monitoring of electric load, to achieve these goals, the technical solution used in the present invention is as follows:
nullA kind of Electric Load Prediction System,It is characterized in that: include electric power load control unit、Electric energy acquisition unit、Distribution network automated service unit、Electric power marketing MIS service unit、Transforming plant protecting center cell and power-management centre service unit,Described electric power load control unit respectively with electric energy acquisition unit、Distribution automation system service unit、Electric power marketing MIS service unit、Transforming plant protecting center cell and power-management centre service unit are attached,Described electric power load control unit obtains respectively and processes, with computational analysis, the power transmission parameter that electric energy acquisition unit gathers from electrical network、Transforming plant protecting center cell output quality of power supply energy consumption parameter、Distribution automation system takes the distribution supplemental characteristic of unit output,And provide information on load for Electric power marketing MIS service unit,And carry out conveying to power-management centre service unit and realize fault location、Diagnosis report,And realize Fault Isolation、Load shifts、Field failure maintenance etc. are reported,The load forecast result of described Electric power marketing MIS service unit is scheduling distribution by the transmission of electric power load control unit to the service unit of power-management centre,Described Electric power marketing MIS service unit includes electricity consumption subscriber information management module、Electric load tracking module、Electric power power structure analysis module、Electric load early warning module and electric load decision-making module,Described electricity consumption subscriber information management module passes sequentially through electric load tracking module、Electric power power structure analysis module、Electric load early warning module and electric load decision-making module connect.
nullPreferably,Described electric power load control unit includes embedded Communication processor、Network interface card extending controller、UART interface circuit、SDRAM memory、Clock reference circuit、COM communication controler、Flash storage、LCD display、CAN interface circuit and ethernet interface,Described state embedded Communication processor respectively with ethernet interface、UART interface circuit、SDRAM memory、Clock reference circuit、CAN interface circuit、COM communication controler、Flash storage and network interface card extending controller connect,Described network interface card extending controller is connected with distribution network automated service unit,Described ethernet interface is connected with Electric power marketing MIS service unit,Described UART interface circuit is connected with LCD display,Described CAN interface circuit is connected with electric energy acquisition unit.
Preferably, described embedded Communication processor is by pci bus and network interface card extending controller, flash storage, the communication connection of COM communication controler, and described embedded Communication processor is connected with CAN interface circuit communication by EX bus.
Preferably, described distribution automation system takes unit for historical data management, the drafting of decision implement data acquisition, supervision control, fault location and warning, sequence of events recording, statistical computation and electric load curve, at least includes SCADA module, distribution geography information module and dsm module.
A kind of Methods of electric load forecasting, it is characterised in that: comprise the following steps:
Step 1: collect power consumer information in units of power network line title, and obtain basic electrical parameter from distribution network line, set up grid power circuit power load information database, the power consumer information of collection is stored in electricity consumption subscriber information management module;Being be collected classification analysis to industry-by-industry classification, sale of electricity classification, particular group, appointment user to whole electricity market power consumer information and classification, it mainly includes load curve and typical curve and coulometric analysis etc.;
Step 2: the unusual action information factor that electric load can be impacted by electric load tracking module according to the user power utilization amount historical data on distribution network line is monitored, the factor that electric load information is impacted carries out seasonal adjustment process, to obtain the every prediction index affecting electric load;
Step 3: each prediction index is screened by electric load decision-making module by linear regression model (LRM), the power information of power consumer is tracked and inquiry, reject the impact of distribution network line load factor and calendar effect, carry out line load computational analysis to process, obtain the best trend of electric load change, obtain power distribution network every circuit short-term load forecasting curve, distribution network circuit electricity market in each season in predetermined period is planned and predicted.
Preferably, described seasonal adjustment processes to be undertaken building by X-12-ARIMA seasonal adjustment method and changes stable prediction index data, in order to analyze the feature of electrity market seasonal move and the correct basic trend estimated and reflect season power consumption.Sequence after processed can reflect every profession and trade power consumption Secular Variation Tendency and fluctuating margin;
Described X-12-ARIMA seasonal adjustment method model calculation expression is:
In formula (61), ytRepresent trade power consumption amount, the linear transformation of λ=1 correspondence, λ=0 correspondence logarithmic transformation, there is smooth variation with λ in all the other conversion, as economic indicator sequence, dtIt is a divisor sequence, YtRepresent actual power consumption.
Preferably, described linear regression model (LRM) calculation expression is:
In formula (71), ytRepresenting trade power consumption amount, x represents Industrial Cycle index, and r represents that lag period, c represent constant term, t express time, ztRepresent stochastic error.
In sum, due to the fact that and have employed above technical scheme, the present invention has following remarkable result:
(1), the present invention fully judge the state of development of power load, by the analysis to historical development rule and the assurance to present situation, find the leading power consumption index of market development in time, and reasonable assessment given in the impact that this power load is produced.
(2), the present invention electric power electricity consumption can be carried out Real-time Collection tracking and power structure solution is analyzed, to reviewing with electric load field cause of fluctuation and extrinsic factor impact evaluation analysis, it is achieved the monitoring of electric power power consumption and prediction.
(3), the present invention can in time, comprehensively monitor the fluctuation situation of change of power consumption/load, ensure that power equipment long time stability runs, strong, finer support is provided for each electric power power consumption/Power system load data, for load forecast and monitoring, for realizing the continuous data transmission to various kinds of equipment power consumption, and save the engineering such as a large amount of human cost and track remodelling and become to provide decision support
Accompanying drawing explanation
In order to be illustrated more clearly that present example or technical scheme of the prior art, introduce accompanying drawing required in embodiment or description of the prior art is done simply below, apparently, accompanying drawing in the following describes is only some examples of the present invention, to those skilled in the art, under not paying creationary premise, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the schematic diagram of a kind of Electric Load Prediction System of the present invention.
Fig. 2 is the electric power load control unit schematic diagram of a kind of Electric Load Prediction System of the present invention.
Fig. 3 is the Analysis And Computation Division reason flow chart of a kind of Methods of electric load forecasting of the present invention.
Fig. 4 is the schematic diagram of the Electric power marketing MIS service unit of a kind of Electric Load Prediction System of the present invention.
Fig. 5 is that non-general industrial user in January, 2010 of a kind of Methods of electric load forecasting of the present invention is to the actual power consumption curve chart of in December, 2014.
Fig. 6 is the electricity needs progress curve figure after non-general industrial user's X-12-ARIMA seasonal adjustment program adjustment of a kind of Methods of electric load forecasting of the present invention.
Fig. 7 is that commercial user in January, 2010 of a kind of Methods of electric load forecasting of the present invention is to the actual power consumption curve chart of in December, 2014.
Fig. 8 is the electricity needs progress curve figure after commercial user's X-12-ARIMA seasonal adjustment program adjustment of a kind of Methods of electric load forecasting of the present invention,
Fig. 9 is that agricultural production in January, 2010 of a kind of Methods of electric load forecasting of the present invention is to the actual power consumption curve chart of in December, 2014.
Figure 10 is the electricity needs progress curve figure after the agricultural production X-12-ARIMA seasonal adjustment program adjustment of a kind of Methods of electric load forecasting of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawing in present example, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in invention, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, broadly fall into the scope of protection of the invention.
nullBelow in conjunction with accompanying drawing 1 and Fig. 2,A kind of Electric Load Prediction System,Including electric power load control unit、Electric energy acquisition unit、Distribution network automated service unit、Electric power marketing MIS service unit、Transforming plant protecting center cell and power-management centre service unit,Described electric power load control unit respectively with electric energy acquisition unit、Distribution automation system service unit、Electric power marketing MIS service unit、Transforming plant protecting center cell and power-management centre service unit are attached,Described electric power load control unit obtains respectively and processes, with computational analysis, the power transmission parameter that electric energy acquisition unit gathers from electrical network、Transforming plant protecting center cell output quality of power supply energy consumption parameter、Distribution automation system takes the distribution supplemental characteristic of unit output,And provide information on load for Electric power marketing MIS service unit,And carry out conveying to power-management centre service unit and realize fault location、Diagnosis report,And realize Fault Isolation、Load shifts、Field failure maintenance etc. are reported,The load forecast result of described Electric power marketing MIS service unit is scheduling distribution by the transmission of electric power load control unit to the service unit of power-management centre;Described distribution automation system takes unit for historical data management, the drafting of decision implement data acquisition, supervision control, fault location and warning, sequence of events recording, statistical computation and electric load curve, at least includes SCADA module, distribution geography information module (GIS) and dsm module (DSM).Described Electric power marketing MIS service unit includes electricity consumption subscriber information management module, electric load tracking module, electric power power structure analysis module, electric load early warning module and electric load decision-making module, and described electricity consumption subscriber information management module passes sequentially through electric load tracking module, electric power power structure analysis module, electric load early warning module and electric load decision-making module and connects.
nullIn embodiments of the present invention,As shown in Figure 3,Described electric power load control unit includes embedded Communication processor、Network interface card extending controller、UART interface circuit、SDRAM memory、Clock reference circuit、COM communication controler、Flash storage、LCD display、CAN interface circuit and ethernet interface,Described state embedded Communication processor respectively with ethernet interface、UART interface circuit、SDRAM memory、Clock reference circuit、CAN interface circuit、COM communication controler、Flash storage and network interface card extending controller connect,Described network interface card extending controller is connected with distribution network automated service unit,Described ethernet interface is connected with Electric power marketing MIS service unit,Described UART interface circuit is connected with LCD display,Described CAN interface circuit is connected with electric energy acquisition unit.Described embedded Communication processor is by pci bus and network interface card extending controller, flash storage, the communication connection of COM communication controler, and described embedded Communication processor is connected with CAN interface circuit communication by EX bus.
In the present invention, described electric energy acquisition unit includes current sensor as shown in Figure 3, voltage sensor or intelligent electric meter, electric energy acquisition unit obtains the parameter signals such as accurate voltage electric current and sends to embedded Communication processor from electrical network, obtain the parameters such as accurate voltage electric current from electrical network and include basic electrical parameter, harmonic voltage electric current, frequency and voltage deviation, imbalance and order components, the parameter such as voltage pulsation and flickering, send embedded Communication processor and be calculated and analyze and process the load curve obtaining electrical network, voltage, electric current, frequency, power, DC component, harmonic detecting, dead electricity detects, the data such as dead electricity power record ripple.The parameter of described transforming plant protecting center cell output includes the parameters such as transmission line equipment parameter, transformer equipment parameter, power capacitor parameter, asynchronous machine for calculating quality of power supply energy consumption data.Described distribution automation system server is based on SCADA system, distribution GIS-Geographic Information System (GIS) and dsm (DSM), implement data acquisition according to obtaining the parameters such as the voltage of power distribution network, power factor, electric current, monitor control, fault location and warning, sequence of events recording and statistical computation, then pass through electric power load control unit to carry out conveying to power-management centre service unit and realize fault location, diagnosis report, and realize the reports such as Fault Isolation, load transfer, field failure maintenance.The data of described electric energy acquisition unit, distribution network automated service list and first transforming plant protecting center cell are conveyed into electric power load control unit and are calculated processing; thering is provided multiple electricity/information on load for Electric power marketing MIS service unit, the data that electric power load control unit is calculated processing include 3 seconds real time datas, 3 minutes statistical values and different interval statistical value;Wherein, different interval statistical value include a day statistical interval value, week statistical interval, the moon statistical interval value, season statistical interval value and year statistical interval value power consumption/load, rate of change, chain rate rate of change etc. on year-on-year basis graphic analyses, the data of computing are stored in local flash storage, can be analyzed processing by ethernet interface transmission to Electric power marketing MIS service unit by these power information datas of storage.Can be analyzed processing and load prediction to the power information data obtained by ethernet interface transmission to Electric power marketing MIS service unit by these energy consumption datas of storage, then carry out power scheduling and distribution again through being transmitted to power-management centre service unit by ethernet interface, electric power load control unit.
As shown in Figure 4, described Electric power marketing MIS service unit includes electricity consumption subscriber information management module, electric load tracking module, electric power power structure analysis module, electric load early warning module and electric load decision-making module, and described electricity consumption subscriber information management module passes sequentially through electric load tracking module, electric power power structure analysis module, electric load early warning module and electric load decision-making module and connects.Each user is carried out the operation work of electricity price/business such as electricity bill control, load management by described electricity consumption subscriber information management module, and inquires about relevant user geographical position, automatically generates various power supply plan.nullWhole electric power power consumption/load is predicted analyzing by power department staff by Electric power marketing MIS service unit,First,Power consumer information is managed、Quantitative statistics analyses and prediction,And ensure data source integrity and accuracy,Secondly,Electric load tracking module is used for the influence factor of electric power variation fluctuation and electric power power consumption/load data is tracked and inquiry,Grasp the major influence factors of electric load change exactly,Electric power power structure analysis module is for analyzing the fluctuation of electricity/load、(area electricity/electric structure resolves and industry electricity/load configuration resolves for extrinsic factor fluctuation and power structure,It is subject to the impact such as meteorology of various correlative factor、Mineral products、Factory of enterprise、Agricultural production、Residential electricity consumption、The factors such as the power plant for self-supply impact on load),Described electric load early warning module provides intelligentized electric load early warning and carries out automatically to the change of electric load、Periodic monitoring,To obtain the every prediction index affecting electric load,Prediction index includes load curve,Minimum load,Maximum load,Average load;Once find have ANOMALOUS VARIATIONS namely to send alarm, to relevant staff with prompting.Described electric load decision-making module for whole electric load early warning, fluctuating characteristic be analyzed judge, generate prediction electric load market trend report, it is proposed to corresponding prediction scheme provides electric power power consumption/load Analysis flexible, effective and prediction for power department.
Shown in Fig. 1, Fig. 2, Fig. 3 and Fig. 4, a kind of Methods of electric load forecasting, comprise the following steps:
Step 1: collect power consumer information in units of power network line title, and from distribution network line, obtain basic electrical parameter, set up grid power circuit power load information database, and from electric energy acquisition unit, obtain collection power consumer information in units of power network line title, the power consumer information of collection is stored in electricity consumption subscriber information management module, and be to industry-by-industry classification to whole electricity market power consumer information and classification, sale of electricity classification, particular group, user is specified to be collected classification analysis, it mainly includes load curve and typical curve and coulometric analysis etc., coulometric analysis is to industry-by-industry classification, sale of electricity classification, particular group, user is specified to be analyzed.The chart carrying out electricity, on year-on-year basis rate of change, chain rate rate of change etc. at different time latitudes such as day, the moon, season, years divides, and proportion, rate of increase, contrast, history chain rate, client's ranking etc. over the years.
Wherein, load curve and typical curve include:
(a), with day for time dimension, Macro or mass analysis different industries classification, sale of electricity classification, particular group, appointment user daily load curve.
B () analyzes typical load curve with the moon, season for time dimension.
Step 2: the unusual action information factor that electric load can be impacted by electric load tracking mould according to the user power utilization amount historical data on distribution network line is monitored, the factor that electric load information is impacted carries out seasonal adjustment process, to obtain the every prediction index affecting electric load;Influence factor that electric power variation is fluctuated by electric load tracking module in the present invention and electric power power consumption/load data is tracked and inquiry, and the factor that change carries out automatically, periodic monitoring, electric load information impact of electric load is carried out seasonal adjustment process, to obtain the every prediction index affecting electric load;Grasp the major influence factors of electric load change exactly, electric power power structure analysis module is for analyzing the fluctuation of electricity/load, extrinsic factor fluctuation and power structure (area electricity/electric structure resolves and industry electricity/load configuration resolves, and is subject to the impact such as the factor impacts on load such as meteorology, mineral products, factory of enterprise, agricultural production, residential electricity consumption, power plant for self-supply of various correlative factor).
Step 3: each prediction index is screened by electric load decision-making module by linear regression model (LRM), the power information of power consumer is tracked and inquiry, reject the impact of distribution network line load factor and calendar effect, carry out line load computational analysis to process, obtain the best trend of electric load change, obtain power distribution network every circuit short-term load forecasting curve, distribution network circuit electricity market in each season in predetermined period is planned and predicted;So that analyzing the feature of electrity market seasonal move and the correct basic trend estimated and reflect season power consumption.Electric load decision-making module in the present invention for whole electric load early warning, fluctuating characteristic be analyzed judge, by linear regression model (LRM), each prediction index is screened, generate prediction electric load market trend report, it is proposed to corresponding prediction scheme provides electric power power consumption/load Analysis flexible, effective and prediction for power department.
As highly preferred embodiment of the present invention, described seasonal adjustment processes and is undertaken building the prediction index data that change is stable by X-12-ARIMA seasonal adjustment method, and the sequence after processed can reflect every profession and trade power consumption Secular Variation Tendency and fluctuating margin;Described X-12-ARIMA seasonal adjustment method model calculation expression is:
In formula (61), ytRepresent trade power consumption amount, the linear transformation of λ=1 correspondence, λ=0 correspondence logarithmic transformation, there is smooth variation with λ in all the other conversion, as economic indicator sequence, dtIt is a divisor sequence, YtRepresent actual power consumption.
Described divisor sequence dtMeet symmetry change: first it is sought symmetrical rate of change Ci(t):
Ci(t)=200 × [di(t)-di(t-1)]/[di(t)+di(t-1)](62);
In formula (62), when taking 0 or negative value when composing indexes, or when Index Content is ratio, then take the difference of this index adjacent moment, namely meet:
Ci(t)=di(t)-di(t-1)(63);
C in formula (63)i(t)、diT () is the i-th index value in the moment respectively.
In embodiments of the present invention, by linear regression, each prediction index being carried out screening and remove exceptional value and calendar effect, obtain the best trend of the change of electric load, described linear regression model (LRM) calculation expression is:
In formula (71), ytRepresenting trade power consumption amount, x represents Industrial Cycle index, and r represents that lag period, c represent constant term, t express time, ztRepresent stochastic error.
The present invention was for 2010 2014 Guangxi province main industries electricity consumption initial datas, the electricity consumption of non-general industry, business, resident living and agricultural production is carried out period forecasting, the data gathered are as shown in table 1, the electricity consumption data gathered carry out the data after seasonal periodicity adjustment as shown in table 2.
Table 1:2010 Guangxi province main industries electricity consumption initial data (unit: hundred million kilowatt hours) in 2014
Data (unit: hundred million kilowatt hours) after table 2:2010 Guangxi province main industries electricity consumption seasonal adjustment in 2014
The general industrial user of Fig. 5 right and wrong in January, 2010 is to the actual power consumption of in December, 2014, electricity needs progress curve after the adjustment of Fig. 6 right and wrong general industrial user X-12-ARIMA seasonal adjustment program, comparison diagram 5 and Fig. 6 be to 2010 2014 I, II, III, be predicted in IV season processing, the best trend of electric load change as we know from the figure, can carry out the trend of the electricity needs development of load prediction according to change curve.
Fig. 7 is commercial user in January, 2010 to the actual power consumption of in December, 2014, Fig. 8 is the electricity needs progress curve after commercial user's X-12-ARIMA seasonal adjustment program adjusts, comparison diagram 7 and Fig. 8 be to 2010 2014 I, II, III, be predicted in IV 4 season processing, the best trend of electric load seasonal periodicity change as we know from the figure, can carry out the trend of the electricity needs development of load prediction according to change curve.
Fig. 9 is agricultural production in January, 2010 to the actual power consumption of in December, 2014, electricity needs progress curve after the adjustment of Figure 10 agricultural production X-12-ARIMA seasonal adjustment program, comparison diagram 9 and Figure 10 be to 2010 2014 I, II, III, be predicted in IV 4 season processing, the best trend of electric load seasonal periodicity change as we know from the figure, can carry out the trend of the electricity needs development of load prediction according to change curve.
The foregoing is only the preferred embodiment of invention, not in order to limit the present invention, all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (7)
- null1. an Electric Load Prediction System,It is characterized in that: include electric power load control unit、Electric energy acquisition unit、Distribution network automated service unit、Electric power marketing MIS service unit、Transforming plant protecting center cell and power-management centre service unit,Described electric power load control unit respectively with electric energy acquisition unit、Distribution automation system service unit、Electric power marketing MIS service unit、Transforming plant protecting center cell and power-management centre service unit are attached,Described electric power load control unit obtains respectively and processes, with computational analysis, the power transmission parameter that electric energy acquisition unit gathers from electrical network、Transforming plant protecting center cell output quality of power supply energy consumption parameter、Distribution automation system takes the distribution supplemental characteristic of unit output,And provide information on load for Electric power marketing MIS service unit,And carry out conveying to power-management centre service unit and realize fault location、Diagnosis report,And realize Fault Isolation、Load shifts、Field failure maintenance etc. are reported,The load forecast result of described Electric power marketing MIS service unit is scheduling distribution by the transmission of electric power load control unit to the service unit of power-management centre,Described Electric power marketing MIS service unit includes electricity consumption subscriber information management module、Electric load tracking module、Electric power power structure analysis module、Electric load early warning module and electric load decision-making module,Described electricity consumption subscriber information management module passes sequentially through electric load tracking module、Electric power power structure analysis module、Electric load early warning module and electric load decision-making module connect.
- null2. a kind of Electric Load Prediction System according to claim 1,It is characterized in that: described electric power load control unit includes embedded Communication processor、Network interface card extending controller、UART interface circuit、SDRAM memory、Clock reference circuit、COM communication controler、Flash storage、LCD display、CAN interface circuit and ethernet interface,Described state embedded Communication processor respectively with ethernet interface、UART interface circuit、SDRAM memory、Clock reference circuit、CAN interface circuit、COM communication controler、Flash storage and network interface card extending controller connect,Described network interface card extending controller is connected with distribution network automated service unit,Described ethernet interface is connected with Electric power marketing MIS service unit,Described UART interface circuit is connected with LCD display,Described CAN interface circuit is connected with electric energy acquisition unit.
- 3. a kind of Electric Load Prediction System according to claim 2, it is characterized in that: described embedded Communication processor is by pci bus and network interface card extending controller, flash storage, the communication connection of COM communication controler, and described embedded Communication processor is connected with CAN interface circuit communication by EX bus.
- 4. a kind of Electric Load Prediction System according to claim 1, it is characterized in that: described distribution automation system takes unit for historical data management, the drafting of decision implement data acquisition, supervision control, fault location and warning, sequence of events recording, statistical computation and electric load curve, at least includes SCADA module, distribution geography information module and dsm module.
- 5. a Methods of electric load forecasting, it is characterised in that: comprise the following steps:Step 1: collect power consumer information in units of power network line title, and obtain basic electrical parameter from distribution network line, set up grid power circuit power load information database, the power consumer information of collection is stored in electricity consumption subscriber information management module;Step 2: the unusual action information factor that electric load can be impacted by electric load tracking module according to the user power utilization amount historical data on distribution network line is monitored, the factor that electric load information is impacted carries out seasonal adjustment process, to obtain the every prediction index affecting electric load;Step 3: each prediction index is screened by electric load decision-making module by linear regression model (LRM), the power information of power consumer is tracked and inquiry, reject the impact of distribution network line load factor and calendar effect, carry out line load computational analysis to process, obtain the best trend of electric load change, obtain power distribution network every circuit short-term load forecasting curve, distribution network circuit electricity market in each season in predetermined period is planned and predicted.
- 6. a kind of Methods of electric load forecasting according to claim 5, it is characterized in that: described seasonal adjustment processes and undertaken building the prediction index data that change is stable by X-12-ARIMA seasonal adjustment method, and described X-12-ARIMA seasonal adjustment method model calculation expression meets:In formula (61), ytRepresenting trade power consumption amount, the linear transformation of λ=1 correspondence, λ=0 correspondence logarithmic transformation, there is smooth variation, d with λ in all the other conversiontIt is a divisor sequence, YtRepresent actual power consumption.
- 7. a kind of Methods of electric load forecasting according to claim 5, it is characterised in that: described linear regression model (LRM) calculation expression is:In formula (71), ytRepresenting trade power consumption amount, x represents Industrial Cycle index, and r represents that lag period, c represent constant term, t express time, ztRepresent stochastic error.
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102855525A (en) * | 2012-08-16 | 2013-01-02 | 中国电力科学研究院 | System and method for forecasting and analyzing load of resident user |
CN204156611U (en) * | 2014-11-15 | 2015-02-11 | 国网河南省电力公司开封供电公司 | A kind of intelligent distribution network data management and analytical equipment |
US20150207319A1 (en) * | 2005-06-06 | 2015-07-23 | Gridpoint, Inc. | Optimized energy management system |
CN104967123A (en) * | 2015-08-03 | 2015-10-07 | 苏州健雄职业技术学院 | Intermittence energy consumption under power distribution network monitoring device and monitoring method |
US20150295407A1 (en) * | 2014-04-10 | 2015-10-15 | Nec Laboratories America, Inc. | Decentralized Energy Management Platform |
-
2016
- 2016-03-22 CN CN201610163622.5A patent/CN105811402B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150207319A1 (en) * | 2005-06-06 | 2015-07-23 | Gridpoint, Inc. | Optimized energy management system |
CN102855525A (en) * | 2012-08-16 | 2013-01-02 | 中国电力科学研究院 | System and method for forecasting and analyzing load of resident user |
US20150295407A1 (en) * | 2014-04-10 | 2015-10-15 | Nec Laboratories America, Inc. | Decentralized Energy Management Platform |
CN204156611U (en) * | 2014-11-15 | 2015-02-11 | 国网河南省电力公司开封供电公司 | A kind of intelligent distribution network data management and analytical equipment |
CN104967123A (en) * | 2015-08-03 | 2015-10-07 | 苏州健雄职业技术学院 | Intermittence energy consumption under power distribution network monitoring device and monitoring method |
Cited By (20)
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CN111314162A (en) * | 2019-11-29 | 2020-06-19 | 中国电力科学研究院有限公司 | Method and system for performing drive test on power service based on drive test instrument |
CN112232559A (en) * | 2020-10-12 | 2021-01-15 | 国网江西省电力有限公司信息通信分公司 | Short-term prediction method and device for load in power area |
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CN114172265A (en) * | 2021-11-23 | 2022-03-11 | 保定市冀能电力自动化设备有限公司 | Comprehensive protection measurement and control system |
CN115062869A (en) * | 2022-08-04 | 2022-09-16 | 国网山东省电力公司东营供电公司 | Comprehensive energy scheduling method and system considering carbon emission |
CN115062869B (en) * | 2022-08-04 | 2022-12-09 | 国网山东省电力公司东营供电公司 | Comprehensive energy scheduling method and system considering carbon emission |
CN116132548A (en) * | 2023-02-06 | 2023-05-16 | 北京思维实创科技有限公司 | Data acquisition method and system for distributed multi-source heterogeneous data |
CN116132548B (en) * | 2023-02-06 | 2023-08-15 | 北京思维实创科技有限公司 | Data acquisition method and system for distributed multi-source heterogeneous data |
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