CN107506872A - A kind of residential block part throttle characteristics and the Categorical research method of model prediction - Google Patents
A kind of residential block part throttle characteristics and the Categorical research method of model prediction Download PDFInfo
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Abstract
The present invention relates to a kind of residential block part throttle characteristics and the Categorical research method of model prediction.By establishing the state space of residential block, residential block is divided into different classifications, the load condition of residential block to be predicted is fitted by the part throttle characteristics of other similar residential blocks.The present invention holds the characteristic rule of development, scientifically formulates electricity consumption planning；The prediction load that forecast model can be easily and effectively is established in classification, contributes to the allotment for being powered net of science, preferably causes the operation of power supply network economic security.
Description
Technical field
The present invention relates to residential electricity consumption load field, and in particular to the classification of a kind of residential block part throttle characteristics and model prediction
Research method.
Background technology
With the fast development of China's economy and the further in-depth of power system reform, Electricity market analysis works to electricity
The operation and planning of power enterprise are more and more important.Load Characteristic Analysis and investigation be Power System Planning basis, be understand and
Predict the necessary means in user and market in compass of competency.Resident load is the important component of city load, and city is occupied
People's part throttle characteristics is investigated, to resident load prediction, the formulation of residential block power supply plan, urban power network planning, power network
Economical operation and Marketing of Power Market are significant.By being analysed in depth to region part throttle characteristics, this is understood in depth
The part throttle characteristics situation in region, effective load monitoring can be carried out, improve the electricity consumption situation of Demand-side, make whole network load
It is more steady, so as to improve social benefit.Load Prediction In Power Systems temporally difference can be divided into long-term, mid-term, it is short-term and
Ultra-short term is predicted.The method of research has Regression Forecast, trend extrapolation, time series method, neural net method etc..If choosing
The standard for selecting forecast model is to pursue the maximization of precision of prediction, then is preferably selected time series models.Time Series Method is pre-
Surveying the basic ideas of electric load is：A large amount of accurate historical datas are collected, according to possessed by following and time in the past sequence
Similitude, its rule changed over time is disclosed by historical load data, establishes the model of science, examined in large quantities from
And continuous sophisticated model, reach optimal prediction result.
There are many fruitful research work for the Load Characteristic Analysis of power system, although these achievements in research pair
Regional load specificity analysis has certain reference, but there is also respective deficiency and limitation.
The content of the invention
It is an object of the invention to provide a kind of residential block part throttle characteristics and the Categorical research method of model prediction, this method
By going to predict the load of newly-built community to the load data of existing residential block or carrying out the load abnormality detection of existing residential block,
Greatly reduce workload, contribute to the allotment for being powered net of science, preferably cause the operation of power supply network economic security.
To achieve the above object, the technical scheme is that：A kind of residential block part throttle characteristics and the classification of model prediction
Research method, comprise the following steps,
S1, residential block feature space is established, divide classification：According to the characteristic of residential block, including regional location, occupation of land face
Product, plot ratio, total amount, house average price, opening quotation time, property fees, building classification, educational resource, life ＆ amusement resource, traffic
Trip data, those data are handled so that each residential block is by one group of character representation to quantize, then to each residence
People area carries out K-MEANS cluster analyses, completes the category division of residential block；
S2, establish ARIMA forecast models and effectively predict load；
S3, establish the prediction of same type cell load：For area for settlement, according to the negative of the similar residential block of existing characteristic index
The load index of lotus data quantification area for settlement, and predict that the load of area for settlement is sent out by the rule of development of existing residential block
Exhibition.
In an embodiment of the present invention, for the similar residential block of two characteristic index, load exception number need to be carried out
According to inspection, even A and B are the similar residential block of two characteristic index, and feature is extracted respectively to two residential blocks, and to A and B
Establish ARIMA forecast models；If the feature of A and B cells is close, part throttle characteristics also can be close, and vice versa；If there is feature
It is close, the mutually remote situation of part throttle characteristics, then it is assumed that occur abnormal.
In an embodiment of the present invention, the specific implementation process of the step S1 is as follows：
Following processing is done to the characteristic data of residential block：
Regional location：Unique numbering is given according to community, street；
Floor space, plot ratio, total amount, house average price, property fees：Normalization, it is 0 ~ 1 to make its codomain；
Open the set the time：The time difference with current time is converted into, the time difference is normalized, it is 0 ~ 1 to make its codomain；
Build classification：Building classification is divided into one or more combination forms in villa, foreign-style house, small high-rise, high level, it is every kind of
Form gives unique numbering；
Educational resource：Educational resource is divided into primary school's quantity, emphasis primary school quantity, common junior middle school's quantity, emphasis junior middle school quantity,
Weights normalize after calculating not assign 3,3.5,4,4.5, make its codomain for 0 ~ 1；
Life ＆ amusement resource：Life ＆ amusement resource is divided into bank quantity, restaurant quantity, cinema's quantity, market quantity, synthesis
Quantity weights normalize after calculating not assign 1,1,5,10,20, make its codomain for 0 ~ 1；
Traffic trip：Traffic trip is divided into bus, railway station, works as bus routes<When 5, it is worth for 0；Bus routes>When 5,
It is worth for 0.5；It is 1 to have railway station duration；
After the above-mentioned processing of characteristic data progress to residential block so that each residential block is by one group of mark sheet to quantize
Show, K-MEANS cluster analyses then are carried out to each residential block, complete the category division of residential block.
Compared to prior art, the invention has the advantages that：The present invention passes through the load number to existing residential block
According to the load abnormality detection for going to predict the load of newly-built community or carry out having residential block, greatly reduce workload, contribute to section
The allotment for being powered net learned, preferably cause the operation of power supply network economic security.
Brief description of the drawings
Fig. 1 is the prediction flow chart based on characteristic index consistent with the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawings, technical scheme is specifically described.
A kind of residential block part throttle characteristics of the present invention and the Categorical research method of model prediction, comprise the following steps,
S1, residential block feature space is established, divide classification：According to the characteristic of residential block, including regional location, occupation of land face
Product, plot ratio, total amount, house average price, opening quotation time, property fees, building classification, educational resource, life ＆ amusement resource, traffic
Trip data, those data are handled so that each residential block is by one group of character representation to quantize, then to each residence
People area carries out K-MEANS cluster analyses, completes the category division of residential block；
S2, establish ARIMA forecast models and effectively predict load；
S3, establish the prediction of same type cell load：For area for settlement, according to the negative of the similar residential block of existing characteristic index
The load index of lotus data quantification area for settlement, and predict that the load of area for settlement is sent out by the rule of development of existing residential block
Exhibition.
For the similar residential block of two characteristic index, the inspection of load abnormal data need to be carried out, even A and B are
The similar residential block of two characteristic index, feature is extracted respectively to two residential blocks, and ARIMA forecast models are established to A and B；
If the feature of A and B cells is close, part throttle characteristics also can be close, and vice versa；If it is close feature occur, part throttle characteristics is mutually remote
Situation, then it is assumed that occur abnormal.
The specific implementation process of the step S1 is as follows：
Following processing is done to the characteristic data of residential block：
Regional location：Unique numbering is given according to community, street；
Floor space, plot ratio, total amount, house average price, property fees：Normalization, it is 0 ~ 1 to make its codomain；
Open the set the time：The time difference with current time is converted into, the time difference is normalized, it is 0 ~ 1 to make its codomain；
Build classification：Building classification is divided into one or more combination forms in villa, foreign-style house, small high-rise, high level, it is every kind of
Form gives unique numbering；
Educational resource：Educational resource is divided into primary school's quantity, emphasis primary school quantity, common junior middle school's quantity, emphasis junior middle school quantity,
Weights normalize after calculating not assign 3,3.5,4,4.5, make its codomain for 0 ~ 1；
Life ＆ amusement resource：Life ＆ amusement resource is divided into bank quantity, restaurant quantity, cinema's quantity, market quantity, synthesis
Quantity weights normalize after calculating not assign 1,1,5,10,20, make its codomain for 0 ~ 1；
Traffic trip：Traffic trip is divided into bus, railway station, works as bus routes<When 5, it is worth for 0；Bus routes>When 5,
It is worth for 0.5；It is 1 to have railway station duration；
After the above-mentioned processing of characteristic data progress to residential block so that each residential block is by one group of mark sheet to quantize
Show, K-MEANS cluster analyses then are carried out to each residential block, complete the category division of residential block.
It is below the specific implementation process of the present invention.
The residential block part throttle characteristics of the present invention and the Categorical research method of model prediction, comprise the following steps：
（1）Residential block feature space is established, divides classification
The characteristic of residential block includes：Regional location, floor space, plot ratio, total amount, house average price, opening quotation time, thing
Industry is taken, builds classification, educational resource, life ＆ amusement resource, traffic trip, and following processing can be done to these data：
Regional location：Unique numbering is given according to community, street；
Floor space, total amount, house average price, property fees, plot ratio：Normalization, it is 0 ~ 1 to make its codomain；
Open the set the time：The time difference with current time is converted into, the time difference is normalized, it is 0 ~ 1 to make its codomain；
Build classification：Building classification can be divided into villa, foreign-style house, small high-rise, high level, always have 15 kinds of combination forms, every kind of form is given
Give unique numbering；
Educational resource：Educational resource is divided into primary school's quantity, emphasis primary school quantity, common junior middle school's quantity, emphasis junior middle school quantity,
Weights normalize after calculating not assign 3,3.5,4,4.5, make its codomain for 0 ~ 1；
Life ＆ amusement resource：Life ＆ amusement resource is divided into bank quantity, restaurant quantity, cinema's quantity, market quantity, synthesis
Quantity weights normalize after calculating not assign 1,1,5,10,20, make its codomain for 0 ~ 1；
Traffic trip：Traffic trip is divided into bus, railway station, works as bus routes<When 5, it is worth for 0；Bus routes>When 5,
It is worth for 0.5；It is 1 to have railway station duration.
After processing of the data more than, each residential block is described by one group of feature to quantize, and residential block is entered
Row K-MEANS cluster analyses, complete the category division of residential block.
（2）Establish ARIMA forecast models and effectively predict load
So-called ARIMA models, refer to nonstationary time series being converted into stationary time series, then by dependent variable only to it
The present worth and lagged value of lagged value and stochastic error are returned established model.ARIMA models are according to former sequence
The difference of contained part in no steady and recurrence, including moving average process（MA）, autoregressive process（AR）, autoregression movement
Averaging process（ARMA）And ARIMA processes.
（3）Establish the prediction of same type cell load
For newly-built community, by the load index of the load data quantification newly-built community of existing cell, be advantageous to subtract
The load Analysis work of few newly-built community, and the load for holding by the rule of development of existing cell newly-built community exactly is sent out
Exhibition；
For the similar cell of two characteristic index, the inspection of load abnormal data can be carried out.As illustrated, A and B two
Individual cell extracts feature respectively, and establishes forecast model to A and B.If the feature of A and B cells is close, part throttle characteristics also can
Close, vice versa.If occurring, feature is close, the mutually remote situation of part throttle characteristics, and abnormal conditions occurs in consideration.
The present invention principle be：
Space of the invention by establishing residential block feature, carry out division classification.It is suitable to be established by certain cell history data
Forecast model, then this model is also applied for the load prediction of homogeneous cells.Change over time, same cell electricity consumption are special
Property is also varied from, and equally workload can be greatly reduced with classification analysis for this changing rule.
The beneficial effects of the present invention are：
The present invention to the load data by existing residential block by going to predict the load of newly-built community or carrying out existing residential block
Load abnormality detection, greatly reduce workload, contribute to the allotment for being powered net of science, preferably cause power supply network warp
Ji safe operation.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment can
To be realized by software, the mode of necessary general hardware platform can also be added by software to realize.Based on such understanding,
The technical scheme of above-described embodiment can be embodied in the form of software product, the software product can be stored in one it is non-easily
The property lost storage medium（Can be CD-ROM, USB flash disk, mobile hard disk etc.）In, including some instructions are causing a computer to set
It is standby（Can be personal computer, server, or network equipment etc.）Perform the method described in each embodiment of the present invention.
Above is presently preferred embodiments of the present invention, all changes made according to technical solution of the present invention, caused function are made
During with scope without departing from technical solution of the present invention, protection scope of the present invention is belonged to.
Claims (3)
1. a kind of residential block part throttle characteristics and the Categorical research method of model prediction, it is characterised in that：Comprise the following steps,
S1, residential block feature space is established, divide classification：According to the characteristic of residential block, including regional location, occupation of land face
Product, plot ratio, total amount, house average price, opening quotation time, property fees, building classification, educational resource, life ＆ amusement resource, traffic
Trip data, those data are handled so that each residential block is by one group of character representation to quantize, then to each residence
People area carries out K-MEANS cluster analyses, completes the category division of residential block；
S2, establish ARIMA forecast models and effectively predict load；
S3, establish the prediction of same type cell load：For area for settlement, according to the negative of the similar residential block of existing characteristic index
The load index of lotus data quantification area for settlement, and predict that the load of area for settlement is sent out by the rule of development of existing residential block
Exhibition.
2. a kind of residential block part throttle characteristics according to claim 1 and the Categorical research method of model prediction, its feature exist
In：For the similar residential block of two characteristic index, the inspection of load abnormal data need to be carried out, even A and B are two spies
Feature is extracted, and establish ARIMA forecast models to A and B in two residential blocks by the property similar residential block of index respectively；If A and B
The feature of cell is close, then part throttle characteristics also can be close, and vice versa；If occurring, feature is close, the mutually remote situation of part throttle characteristics,
Then think exception occur.
3. a kind of residential block part throttle characteristics according to claim 1 and the Categorical research method of model prediction, its feature exist
In：The specific implementation process of the step S1 is as follows：
Following processing is done to the characteristic data of residential block：
Regional location：Unique numbering is given according to community, street；
Floor space, plot ratio, total amount, house average price, property fees：Normalization, it is 0 ~ 1 to make its codomain；
Open the set the time：The time difference with current time is converted into, the time difference is normalized, it is 0 ~ 1 to make its codomain；
Build classification：Building classification is divided into one or more combination forms in villa, foreign-style house, small high-rise, high level, it is every kind of
Form gives unique numbering；
Educational resource：Educational resource is divided into primary school's quantity, emphasis primary school quantity, common junior middle school's quantity, emphasis junior middle school quantity,
Weights normalize after calculating not assign 3,3.5,4,4.5, make its codomain for 0 ~ 1；
Life ＆ amusement resource：Life ＆ amusement resource is divided into bank quantity, restaurant quantity, cinema's quantity, market quantity, synthesis
Quantity weights normalize after calculating not assign 1,1,5,10,20, make its codomain for 0 ~ 1；
Traffic trip：Traffic trip is divided into bus, railway station, works as bus routes<When 5, it is worth for 0；Bus routes>When 5,
It is worth for 0.5；It is 1 to have railway station duration；
After the above-mentioned processing of characteristic data progress to residential block so that each residential block is by one group of mark sheet to quantize
Show, K-MEANS cluster analyses then are carried out to each residential block, complete the category division of residential block.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491981A (en) * | 2018-04-04 | 2018-09-04 | 国网安徽省电力公司黄山供电公司 | A kind of load forecasting method promoting scenic spot saturation load forecasting precision |
| CN109711621A (en) * | 2018-12-27 | 2019-05-03 | 浙江华云信息科技有限公司 | The industrial park load forecasting method combined based on discriminant analysis and support vector machines |
| CN110674999A (en) * | 2019-10-08 | 2020-01-10 | 国网河南省电力公司电力科学研究院 | Cell load prediction method based on improved clustering and long-short term memory deep learning |
| CN110888913A (en) * | 2019-10-25 | 2020-03-17 | 国网新疆电力有限公司乌鲁木齐供电公司 | Internet of things technology-based intelligent power consumption information analysis system |
| CN111489103A (en) * | 2020-04-28 | 2020-08-04 | 上海积成能源科技有限公司 | System and method for classifying electricity consumption condition of residents based on autoregressive analysis |
| CN117436935A (en) * | 2023-11-30 | 2024-01-23 | 湖北华中电力科技开发有限责任公司 | Regional power consumption prediction method, regional power consumption prediction system, computer equipment and storage medium |
-
2017
- 2017-09-14 CN CN201710825198.0A patent/CN107506872A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491981A (en) * | 2018-04-04 | 2018-09-04 | 国网安徽省电力公司黄山供电公司 | A kind of load forecasting method promoting scenic spot saturation load forecasting precision |
| CN108491981B (en) * | 2018-04-04 | 2021-06-15 | 国网安徽省电力公司黄山供电公司 | Load prediction method for improving scenic spot saturated load prediction precision |
| CN109711621A (en) * | 2018-12-27 | 2019-05-03 | 浙江华云信息科技有限公司 | The industrial park load forecasting method combined based on discriminant analysis and support vector machines |
| CN110674999A (en) * | 2019-10-08 | 2020-01-10 | 国网河南省电力公司电力科学研究院 | Cell load prediction method based on improved clustering and long-short term memory deep learning |
| CN110888913A (en) * | 2019-10-25 | 2020-03-17 | 国网新疆电力有限公司乌鲁木齐供电公司 | Internet of things technology-based intelligent power consumption information analysis system |
| CN110888913B (en) * | 2019-10-25 | 2023-09-22 | 国网新疆电力有限公司乌鲁木齐供电公司 | Intelligent analysis system for electricity consumption based on Internet of things technology |
| CN111489103A (en) * | 2020-04-28 | 2020-08-04 | 上海积成能源科技有限公司 | System and method for classifying electricity consumption condition of residents based on autoregressive analysis |
| CN117436935A (en) * | 2023-11-30 | 2024-01-23 | 湖北华中电力科技开发有限责任公司 | Regional power consumption prediction method, regional power consumption prediction system, computer equipment and storage medium |
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