CN106127360A - A kind of multi-model load forecasting method analyzed based on user personality - Google Patents
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Abstract
The invention discloses a kind of multi-model load forecasting method analyzed based on user personality, linear regression algorithm is used to build load forecasting model based on different user characteristic with time series algorithm, build data model by concrete data analysis algorithm and draw the multiple-factor load prediction analyzed based on user personality, by line load historical data, microclimate historical data, area GDP historical data predicts the line load value of the following same period；Utilize K Means clustering algorithm that line load data are classified, line load data are divided into residential electricity consumption circuit, commercial power circuit, commercial power circuit according to electricity consumption classification.The present invention takes into full account the difference between different electricity consumption type line so that model is more accurate；Consider the impact on load of many factors of influence, by extracting the main constituent of many factors of influence, find out the principal element to loading effects, abandon secondary cause, utilize data analysis algorithm to build based on main affecting factors forecast model.
Description
Technical field
The invention belongs to Power System Planning and traffic control technical field, particularly relate to a kind of based on user personality analysis
Multi-model load forecasting method.
Background technology
Power system development is to today, it has also become the development of the national economy and people's people's livelihood] live in requisite important step,
The effect of power system is the electric energy providing all types of user economy as far as possible and continuing and have good quality.Electric energy is supplied
Interruption, reduce all will have influence on each department of national economy, even cause serious consequence.The size of load and feature,
Either to Power System Planning, energy resources balance, electric power channelling surplus goods to needly areas is all particularly important.Electric power as commodity and its
He compares by commodity, and the feature of its maximum is exactly that electricity commodity can not store, say, that the production of electricity, carries, distribute, consume
Time carry out simultaneously.So the power load in look-ahead supply district is critically important for power supply enterprise.But by
In electric company, local electricity consumption type differs, and including residential electricity consumption, commercial power and commercial power, electric load is again simultaneously
Affected by many factors, such as factors such as temperature, per capita income level, policies and regulations, cause electric load to have uncertain
Property and complexity.So, change and the feature to load, carry out prior prediction, be Power System Planning and operation study
Important content.
The prediction of electric load is exactly the past according to load and deduces now its future values, due to power consumption not
Definitiveness and complexity, then just determine the incomplete accuracy predicted the outcome, simultaneously because load future development is the most true
Qualitative.On the one hand supply line's load is according to the development and change regularly of certain trend；On the other hand, load by numerous because of
The impact of element, the most all it may happen that fluctuate.Therefore, when being predicted line load analyzing, should fully analyze in it
Dependency and law of development, consider the impact of various factors again.Only take into full account the feature of line load, Changing Pattern
And influence factor, the forecast model of load practical situation could be set up, obtain forecast model more accurately.
Summary of the invention
It is an object of the invention to provide a kind of multi-model load forecasting method analyzed based on user personality, it is desirable to provide
The multiple-factor load forecasting method analyzed based on user personality, according to specific business rule, the impact relevant on line load
The factor carries out labor, builds the concrete impact of Different Effects factor pair different user characteristic circuit in conjunction with data analysis algorithm
Relation draws load forecasting model, is accurately predicted the future load value of circuit by forecast model, for line system planning and electricity
Power management and running provide and support and decision-making foundation.
The present invention be achieved in that a kind of based on user personality analyze multi-model load forecasting method, described based on
The multi-model load forecasting method that user personality is analyzed uses linear regression algorithm to build based on different use from time series algorithm
The load forecasting model of family characteristic, builds data model by concrete data analysis algorithm and draws based on user personality analysis many
Factor loading is predicted, by line load historical data, microclimate historical data, area GDP historical data predicts the following same period
Line load value；Utilize K-Means clustering algorithm that line load data are classified, by line load data according to electricity consumption
Classification is divided into residential electricity consumption circuit, commercial power circuit, commercial power circuit.
Further, described multi-model load forecasting method based on user personality analysis comprises the following steps:
Carry out cluster analysis firstly the need of to area, south of a city Power system load data, the electric load of different user characteristic is become
Change is analyzed；The many factors of influence affecting load are carried out correlation analysis, with its dependency of preliminary examinations；
Then many factors of influence are carried out information compression, replace all original variables to go to be analyzed by a few factors；
Finally by the factor variable after dimensionality reduction with different classes of under the load data of Typical Route carry out linear regression
Analyze, when setting up regression model, according to the statistics class description to initial data, the load of Various Seasonal is predicted, as
The really prediction on Load in Summer peak and the prediction of load fluctuation in winter.
Further, described regression model includes:
Residential electricity consumption line load data are associated with Gaseous microembolus and set up load forecasting model by linear regression model (LRM)；
Time series models, by commercial power line load data and Gaseous microembolus；
Electric line load forecasting model and commercial power line load forecast model, commercial power line load data with
GDP association is set up business respectively and is used.
Further, the concrete grammar of the foundation of described regression model is as follows:
Data are classified by step one by the architectural feature of load data own, use the method handle of K-Means cluster
Circuit, by different tagsorts, is divided into residential electricity consumption class circuit, commercial power intermediate item, commercial power category by electric load
Mesh；
Step 2, does dimension-reduction treatment to factor of influence, analyzes the relevant of the meteorological factor independent variable bigger to loading effects
Property, relevance verification carries out factor extraction after passing through thus reaches independent variable dimensionality reduction；
Step 3, uses linear regression model (LRM) to be associated with factor of influence by residential electricity consumption class circuit, passes through computational analysis
Draw the correlation coefficient under Various Seasonal, thus draw the load forecasting model of residential electricity consumption class circuit；
Step 4, uses ARIMA model by business, commercial power class circuit, first source data is carried out stationary test,
If source data does not have stationarity need to do difference processing, then need that data are done autocorrelation and check with partial autocorrelation, logical
The foundation of model is completed after crossing inspection.
The multi-model load forecasting method analyzed based on user personality that the present invention provides, in order to complete the essence of line load
Really prediction, employs linear regression algorithm and builds load forecasting model based on different user characteristic with time series algorithm, real
Show the multiple-factor load prediction analyzed based on user personality.Mainly study Gaseous microembolus, area GDP and line
The concrete incidence relation of road load value, builds data model by concrete data analysis algorithm and draws based on user personality analysis
Multiple-factor load prediction, by line load historical data, microclimate historical data, area GDP historical data prediction is following same
The line load value of phase.
The line load that the present invention is directed to different user characteristic sets up the line load forecast model of correspondence respectively, fully examines
Consider the difference between different electricity consumption type line so that model is more accurate；Consider the impact on load of many factors of influence,
By extracting the main constituent of many factors of influence, find out the principal element to loading effects, abandon secondary cause, utilize data analysis to calculate
Method (linear regression, time series algorithm) builds forecast model based on main affecting factors.
Present invention utilizes K-Means clustering algorithm line load data are classified, by line load data according to
Electricity consumption classification is divided into residential electricity consumption circuit, commercial power circuit, commercial power circuit；Utilize linear regression model (LRM) by residential electricity consumption
Line load data associate with Gaseous microembolus and set up load forecasting model；Time series models are utilized to be born by commercial power circuit
Lotus data and Gaseous microembolus, commercial power line load data associate with GDP sets up the prediction of commercial power line load respectively
Model and commercial power line load forecast model；Utilize different electricity consumption classification line load historical data and factor of influence history
Data embed the following line load value of each load forecasting model prediction；By accurately prediction circuit load value, business department can
To grasp following line load variation tendency, run for circuit on power system planning and power scheduling and data foundation is provided, auxiliary
Decision-making is done by relevant departments.
Accompanying drawing explanation
Fig. 1 is the multi-model load forecasting method flow chart analyzed based on user personality that the embodiment of the present invention provides.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with embodiment, to the present invention
It is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not used to
Limit the present invention.
Below in conjunction with the accompanying drawings the application principle of the present invention is explained in detail.
As it is shown in figure 1, the multi-model load forecasting method based on user personality analysis of the embodiment of the present invention includes following
Step:
S101: carry out cluster analysis firstly the need of to area, south of a city Power system load data, thus to different user characteristic
Electric load change is analyzed；Secondly the many factors of influence affecting load are carried out correlation analysis, with its phase of preliminary examinations
Guan Xing；
S102: then many factors of influence are carried out information compression, to reduce the number of variable, replaces all by a few factors
Original variable goes to be analyzed；
S103: finally by the factor variable after dimensionality reduction with different classes of under the load data of Typical Route carry out linearly
Regression analysis, to reach the prediction to load；When setting up regression model, according to the statistics class description to initial data, permissible
The load of Various Seasonal is predicted, if the prediction on Load in Summer peak, and the prediction of load fluctuation in winter.
The concrete grammar that the present invention builds model is as follows:
1) by load data architectural feature own, data are classified, the method using K-Means cluster, Wo Menke
So that data are divided into several classifications so that the difference within classification is the least, the difference between classification is big, by the south of a city as far as possible
Area electric load by different tagsort, is analyzed reaching the change of the electric load to different user characteristic.Pass through
The clustering algorithm analysis to line load data, is divided into residential electricity consumption class circuit, commercial power intermediate item, commercial power by circuit
Intermediate item；
2) in order to be that load forecasting model has more accuracy, factor of influence need to be done dimension-reduction treatment, analyze load shadow
Ringing the dependency of bigger meteorological factor independent variable, relevance verification carries out factor extraction after passing through thus reaches independent variable fall
Dimension；
3) use linear regression model (LRM) to be associated with factor of influence by residential electricity consumption class circuit, drawn not by computational analysis
With the correlation coefficient under season, thus draw the load forecasting model of residential electricity consumption class circuit；
4) use ARIMA model by business, commercial power class circuit, first source data is carried out stationary test, if
Source data does not have stationarity need to do difference processing, then needs that data are done autocorrelation and checks with partial autocorrelation, by inspection
The foundation of model is completed after testing.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Any amendment, equivalent and the improvement etc. made within god and principle, should be included within the scope of the present invention.
Claims (4)
1. the multi-model load forecasting method analyzed based on user personality, it is characterised in that described divide based on user personality
It is based on different user characteristic negative that the multi-model load forecasting method of analysis uses linear regression algorithm and time series algorithm to build
Lotus forecast model, builds data model by concrete data analysis algorithm and show that the multiple-factor load analyzed based on user personality is pre-
Surveying, by line load historical data, microclimate historical data, area GDP historical data predicts the line load of the following same period
Value；Utilize K-Means clustering algorithm that line load data are classified, line load data are divided into residence according to electricity consumption classification
Civil electric wire road, commercial power circuit, commercial power circuit.
2. the multi-model load forecasting method analyzed based on user personality as claimed in claim 1, it is characterised in that described base
Multi-model load forecasting method in user personality analysis comprises the following steps:
Carry out cluster analysis firstly the need of to area, south of a city Power system load data, the electric load of different user characteristic is changed into
Row is analyzed；The many factors of influence affecting load are carried out correlation analysis, with its dependency of preliminary examinations；
Then many factors of influence are carried out information compression, replace all original variables to go to be analyzed by a few factors；
Finally by the factor variable after dimensionality reduction with different classes of under the load data of Typical Route carry out linear regression analysis,
When setting up regression model, according to the statistics class description to initial data, the load of Various Seasonal is predicted, if summer
The prediction of load peak and the prediction of load fluctuation in winter.
3. the multi-model load forecasting method analyzed based on user personality as claimed in claim 2, it is characterised in that described time
Model is returned to include:
Residential electricity consumption line load data are associated with Gaseous microembolus and set up load forecasting model by linear regression model (LRM)；
Time series models, by commercial power line load data and Gaseous microembolus；
Electric line load forecasting model and commercial power line load forecast model, commercial power line load data are closed with GDP
Connection is set up business respectively and is used.
4. the multi-model load forecasting method analyzed based on user personality as claimed in claim 2, it is characterised in that described time
The concrete grammar returning the foundation of model is as follows:
Data are classified by step one by the architectural feature of load data own, use the method for K-Means cluster electric power
Circuit, by different tagsorts, is divided into residential electricity consumption class circuit, commercial power intermediate item, commercial power intermediate item by load；
Step 2, does dimension-reduction treatment to factor of influence, analyzes the dependency of the meteorological factor independent variable bigger to loading effects, phase
Closing property carries out factor extraction after being verified thus reaches independent variable dimensionality reduction；
Step 3, is used linear regression model (LRM) to be associated with factor of influence by residential electricity consumption class circuit, is drawn by computational analysis
Correlation coefficient under Various Seasonal, draws the load forecasting model of residential electricity consumption class circuit；
Step 4, uses ARIMA model by business, commercial power class circuit, first source data carries out stationary test, source number
Difference processing need to be done according to not having stationarity, then need that data are done autocorrelation and check with partial autocorrelation, after inspection
Complete the foundation of model.
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| CN108304978A (en) * | 2018-05-08 | 2018-07-20 | 国网江西省电力有限公司经济技术研究院 | A kind of mid-term Electric Power Load Forecast method based on data clusters theory |
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| CN108846530A (en) * | 2018-09-28 | 2018-11-20 | 国网上海市电力公司 | One kind being based on the short-term load forecasting method of " cluster-recurrence " model |
| CN109214563A (en) * | 2018-08-30 | 2019-01-15 | 昆明理工大学 | A kind of prediction of precipitation method based on cluster with linear regression |
| CN109227528A (en) * | 2018-10-16 | 2019-01-18 | 工极智能科技（苏州）有限公司 | Adaptive sliding-mode observer method based on information compression estimation |
| CN109255505A (en) * | 2018-11-20 | 2019-01-22 | 国网辽宁省电力有限公司经济技术研究院 | A kind of short-term load forecasting method of multi-model fused neural network |
| CN109376972A (en) * | 2019-01-01 | 2019-02-22 | 中南大学 | A kind of wisdom Power Network Short-Term Electric Load Forecasting method based on block cluster |
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