CN109816164A - A kind of Methods of electric load forecasting - Google Patents
A kind of Methods of electric load forecasting Download PDFInfo
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- CN109816164A CN109816164A CN201910038483.7A CN201910038483A CN109816164A CN 109816164 A CN109816164 A CN 109816164A CN 201910038483 A CN201910038483 A CN 201910038483A CN 109816164 A CN109816164 A CN 109816164A
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Abstract
The present invention relates to a kind of Methods of electric load forecasting, comprising: step S100 obtains the history Power system load data D=[D of n history cycle of current period1,D2,…,Dn], wherein DiFor the Power system load data in the (n+1)th-i before current period periods, 1≤i≤n；Step S200 is based on the history Power system load data D, obtains history high frequency time domain data DH=[DH using frequency spectrum analysis method1,DH2,…,DHM]TWith history low-frequency time-domain data DL=[DL1,DL2,…,DLn]；Step S300 predicts the Power system load data of current period according to the history high frequency time domain data DH and the history low-frequency time-domain data DLWherein pl is the low frequency predicted value obtained based on the history low-frequency time-domain data DL, the phjFor based on the history high frequency time domain data DHjThe high frequency predicted value of acquisition.
Description
Technical field
The present invention relates to power domain more particularly to a kind of prediction techniques of electric load.
Background technique
Load forecast can play critical work to departments such as planning, electricity consumption and scheduling in electric system
With.The external factors own profound such as electric load and the economic level of their location, the industrial structure, policies and regulations, meteorological condition
Complicated relationship, the characteristics of showing periodicity and randomness.On the one hand, there is certain regularity for the variation of electric load
And periodicity；On the other hand, it is influenced by various external factor, electric load embodies the fluctuation for being difficult to hold again.Patent
File CN106503851A discloses a kind of improved Short-Term Load Forecasting Method based on wavelet analysis, including chooses sample
Notebook data carries out pseudo- data monitoring to the sample data of selection and corrects, determines sample set and be normalized, and obtains more flat
Sliding, more accurate sample data and future position load correlation are stronger, but this method only accounts for Short-term characteristic, also not to work
Day is distinguished with nonworkdays with electrical feature.Patent application CN108549960A discloses a kind of 24 hours Electric Load Forecastings
Survey method is acquired and is pre-processed comprising data, and the validity feature gone out by Characteristic Entropy weight computing, combined window is chosen method and chosen
Input data of 24 hours power load situations as DBN network training in set time, determines DBN network structure and foundation
Network model is simultaneously predicted, however, this method also only accounts for Short-term characteristic, the also not electricity consumption to working day and nonworkdays
Feature distinguishes.Patent document CN108736474A discloses Methods of electric load forecasting and device, quasi- according to historical data
Conjunction obtains moment to be predicted corresponding first fit correlation model and the second fit correlation model, is obtained according to the model to be predicted
Moment corresponding first parameter factors and the second parameter factors；Further according to moment to be predicted in each history cycle corresponding load
Moment to be predicted corresponding load power normalized value is calculated in power, the first parameter factors and the second parameter factors.Most
Afterwards, it is obtained according to moment to be predicted in each history cycle corresponding load power normalized value utilization index smoothing algorithm to pre-
Survey moment to be predicted corresponding load power in the period.This method considers history feature, but model utilization index smoothing algorithm,
There is no memory unit, precision is limited, also not being distinguished with electrical feature to working day and nonworkdays.
Summary of the invention
In order to solve the above technical problems, the invention discloses a kind of Methods of electric load forecasting, comprising: step S100 is obtained
Take the history Power system load data D=[D of n history cycle of current period1,D2,…,Dn], wherein DiIt is before current period
The Power system load data in n+1-i period, 1≤i≤n；Step S200 is based on the history Power system load data D, uses frequency spectrum
Analysis method obtains history high frequency time domain data DH=[DH1,DH2,…,DHM]TWith history low-frequency time-domain data DL=[DL1,
DL2,…,DLn]；Step S300, according to the history high frequency time domain data DH and the history low-frequency time-domain data DL, prediction is worked as
The Power system load data in preceding periodWherein pl is low to be obtained based on the history low-frequency time-domain data DL
Frequency predicted value, the phjFor based on the history high frequency time domain data DHjThe high frequency predicted value of acquisition.
Detailed description of the invention
Fig. 1 is a kind of flow chart of Methods of electric load forecasting of the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention will be made further in conjunction with attached drawing
Detailed description.This description is to describe specific implementation consistent with the principles of the present invention by way of example, and not limitation
Mode, the description of these embodiments is detailed enough, so that those skilled in the art can practice the present invention, is not being taken off
Other embodiments can be used in the case where from scope and spirit of the present invention and can change and/or replace each element
Structure.Therefore, the following detailed description should not be understood from restrictive sense.
Fig. 1 is a kind of flow chart of Methods of electric load forecasting of the invention.As shown in Figure 1, this method comprises:
Step S100 obtains the history Power system load data D=[D of n history cycle of current period1,D2,…,Dn],
Wherein DiFor the Power system load data in the (n+1)th-i before current period periods, 1≤i≤n.According to the present invention, the electricity in the period
Power load data is the Power system load data of specified geography fence region, transformer and/or user in different cycles, wherein described
Geography fence region can be obtained according to the division for carrying out the other shapes such as rectangle to terrestrial coordinate system, the transformer and/or institute
Stating user can be specified transformer and/or designated user.The length in the period can customize setting, it is preferred that the length
Value range be [1 day, 100 days].Further, according to the present invention, the value of n can customize setting, it is preferable that the value of n
Range be [10,100], the customized setting of n value, can convenient for as needed obtain in the recent period or temporary electricity load characteristic and/or
Electric load feature when long-term or long, such as when n value is smaller, electric load feature in a short time can be got, if n value
Larger, then what is reflected is long-term electric load feature.Table 1 is totally 97 periods under the transformer Source of Gateway Meter that obtains of the present invention
Power system load data, wherein the length in the period be 1 day, and Power system load data unit be kwh (be limited to length, herein
Value provides partial data):
Table 1
| D1 | D2 | ... | D97 |
| 2869.6 | 2899.2 | ... | 2444.8 |
Step S200 is based on the history Power system load data D, obtains history high frequency time domain number using frequency spectrum analysis method
According to DH=[DH1,DH2,…,DHM]TWith history low-frequency time-domain data DL=[DL1,DL2,…,DLn], wherein M is the frequency spectrum point
The analysis number of plies in analysis method, DHj=[DHj1,DHj2,…,DHjn]TFor the jth layer height obtained according to the frequency spectrum analysis method
Frequency component, DLkThe numerical value for being the first layer low frequency component that is obtained according to the frequency spectrum analysis method at the kth moment, DHjkFor jth
Numerical value of the layer high fdrequency component at the kth moment, 1≤j≤M, 1≤k≤n.According to one embodiment of present invention, the spectrum analysis
Method is Bartlett frequency spectrum analysis method, and in preferred one embodiment of the invention, the frequency spectrum analysis method is small
Wave conversion method.Further, the small wave converting method is become using the wavelet analysis that the small echos class functions such as symN, dbN carry out M layers
It changes, wherein the value range of M is [2,6], and the value range of N is [2,9], it is preferred that in the present invention, the wavelet transformation side
Method converts the Power system load data D wavelet analysis for carrying out M=3 layers using db5 wavelet function.Table 2 is right in the present invention
The Power system load data D carries out the data after 3 layers of wavelet transformation based on db5 wavelet function, and table 3 is of the invention to described
Power system load data D carries out the history high frequency time domain data DH after 3 layers of wavelet transformation based on db5 wavelet function, and table 4 is this hair
The bright history low-frequency time-domain data Power system load data D carried out after 3 layers of wavelet transformation based on db5 wavelet function
DL。
Table 2
| k | 1 | 2 | ... | 97 |
| DL | DL1=2819.7 | DL2=2823.7 | ... | DL97=2209.4 |
| DH1 | DH1,1=70.3024 | DH1,2=23.71 | ... | DH1,97=117.1698 |
| DH2 | DH2,1=0.4114 | DH2,2=35.0899 | ... | DH2,97=191.2215 |
| DH3 | DH3,1=8.7486 | DH3,2=7.8881 | ... | DH3,97=-72.9452 |
Table 3
| k | 1 | 2 | ... | 97 |
| DH1 | DH1,1=70.3024 | DH1,2=23.71 | ... | DH1,97=117.1698 |
| DH2 | DH2,1=0.4114 | DH2,2=35.0899 | ... | DH2,97=191.2215 |
| DH3 | DH3,1=8.7486 | DH3,2=7.8881 | ... | DH3,97=-72.9452 |
Table 4
| k | 1 | 2 | ... | 97 |
| DL | DL1=2819.7 | DL2=2823.7 | ... | DL97=2209.4 |
Step S300, according to the history high frequency time domain data DH and the history low-frequency time-domain data DL, prediction is current
The Power system load data in periodWherein pl is the low frequency obtained based on the history low-frequency time-domain data DL
Predicted value, the phjFor based on the history high frequency time domain data DHjThe high frequency predicted value of acquisition.In an implementation of the invention
In mode,The i.e. described pl is the average value of the history low-frequency time-domain data DL, excellent in of the invention one
It selects in embodiment, pl is obtained based on conventional time series analysis prediction model and the history low-frequency time-domain data DL, wherein
The conventional time series analysis prediction model is autoregression model AR, Moving Average model M A, auto regressive moving average mould
Type ARMA and/or difference ARMA model ARIMA.Since the smoothed data of low frequency can be used to characterize for a long time
With electrical feature, and prolonged periodical power load data stabilization, fluctuate it is small, therefore using conventional time series analysis prediction
Model is predicted just to be able to satisfy requirement.
Further, according to embodiment of the present invention,In a preferred implementation of the invention
In mode, using the length in machine learning method in short-term memory network LSTM model to the history high frequency time domain data DHjInto
Capable prediction, the random fluctuation data of high frequency can be used for characterizing the use electrical feature of short time, in short-term using the length with memory unit
Memory network LSTM model predicts high frequency time domain data, can further improve the precision of prediction.Table 5 is institute of the present invention
Long memory network LSTM model in short-term is stated to high frequency time domain data DHjThe predicted value predicted, table 6 are tradition of the present invention
Predicted value of the Time Series Analysis Forecasting model to the history low-frequency time-domain data DL.
Table 5
| ph1=35.5368 |
| ph2=-65.5428 |
| ph3=-62.7829 |
Table 6
| Pl=2395 |
It therefore, can be with the Power system load data N=2395+ of the current period according to the data of table 5 and table 6
35.5368-65.5428-62.7829=2302.2.
By content disclosed above it is found that the power load of n history cycle before the current period that the present invention will acquire
Lotus data obtain low-frequency time-domain data and multiple high frequency time domain datas by Spectrum Conversion method, wherein utilize low-frequency time-domain number
According to stability characterize the long-term with electrical feature of transformer, geography fence region either user, use multiple high frequency time domains
The stochastic volatility of data is remembered to characterize short-term electrical feature of using further combined with passing logical Time Series Analysis Model and having
Recalling the length of unit, memory network LSTM model respectively counts it was predicted that being led to low-frequency time-domain data and high frequency time domain data in short-term
It crosses and is superimposed the Power system load data that every predicted value obtains current period, so that the electric load number of the current period finally obtained
It is higher according to accuracy.
In one embodiment of the invention, when the length in the period is one,Wherein, M is weight coefficient, and value range is [0,1], it is preferable that the value range of m is
[0.85,0.92].When the period is one, by using the workaday Power system load data mean value of history/non-work of history
The Power system load data mean value for making day is modified basic forecast value, and it is pre- to further improve current period Power system load data
The precision of measured value.
Preferably, in another embodiment of the present invention, a kind of load forecast server is also disclosed, it is described pre-
The available geography fence of server, transformer and/or the Power system load data of user, the server is surveyed further to go back
It, can be real when the computer program is performed including processor and the non-transient storage medium for being stored with computer program
Existing the above step S100- step S300.The load forecast is realized on the server, can save local storage money
Source and computing resource.
In addition, according to disclosed specification of the invention, other realizations of the invention are for those skilled in the art
Significantly.The various aspects of embodiment and/or embodiment can be used for system of the invention individually or with any combination
In method.Specification and example therein should be only be regarded solely as it is exemplary, the actual scope of the present invention and spirit by appended
Claims indicate.
Claims (10)
1. a kind of Methods of electric load forecasting characterized by comprising
Step S100 obtains the history Power system load data D=[D of n history cycle of current period1,D2,…,Dn], wherein Di
For the Power system load data in the (n+1)th-i before current period periods, 1≤i≤n；
Step S200 is based on the history Power system load data D, obtains history high frequency time domain data DH using frequency spectrum analysis method
=[DH1,DH2,…,DHM]TWith history low-frequency time-domain data DL=[DL1,DL2,…,DLn], wherein M is the spectrum analysis side
The analysis number of plies in method, DHj=[DHj1,DHj2,…,DHjn]TFor the high frequency division of jth layer obtained according to the frequency spectrum analysis method
Amount, DLkThe numerical value for being the first layer low frequency component that is obtained according to the frequency spectrum analysis method at the kth moment, DHjkIt is high for jth layer
Numerical value of the frequency component at the kth moment, 1≤j≤M, 1≤k≤n；
Step S300 predicts current period according to the history high frequency time domain data DH and the history low-frequency time-domain data DL
Power system load dataWherein pl is the low frequency prediction obtained based on the history low-frequency time-domain data DL
Value, the phjFor based on the history high frequency time domain data DHjThe high frequency predicted value of acquisition.
2. Methods of electric load forecasting according to claim 1, which is characterized in that the value range of M is [2,6], preferably
It is 3.
3. Methods of electric load forecasting according to claim 1, which is characterized in that the length in the period, which can customize, to be set
It sets, it is preferred that the value range of the length is [1 day, 100 days].
4. Methods of electric load forecasting according to claim 1, which is characterized in that the value of n can customize setting, preferably
Ground, the value range of n are [10,100].
5. Methods of electric load forecasting according to claim 1-4, which is characterized in that the frequency spectrum analysis method
For small wave converting method.
6. Methods of electric load forecasting according to claim 1, which is characterized in that in step S300, when based on tradition
Between sequence analysis prediction model and the history low-frequency time-domain data DL obtain pl；Preferably, the conventional time series analysis
Prediction model can be mobile for autoregression model, rolling average line model, ARMA model and/or difference autoregression
Averaging model.
7. Methods of electric load forecasting according to claim 1, which is characterized in that be based on the history high frequency time domain data
DHjPh is obtained with long memory network LSTM model in short-termj。
8. Methods of electric load forecasting according to claim 1-7, which is characterized in that when the length in the period
When being one,Wherein, M is weight coefficient, and value range is [0,1].
9. Methods of electric load forecasting according to claim 8, which is characterized in that the value range of the m be [0.85,
0.92]。
10. Methods of electric load forecasting according to claim 1, which is characterized in that the Power system load data is specified
The Power system load data of geography fence region, transformer and/or user in different cycles.
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