CN105488590A - Seasonal Kalman filtering model based power load adaptive prediction method - Google Patents
Seasonal Kalman filtering model based power load adaptive prediction method Download PDFInfo
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Abstract
The invention belongs to the technical field of electric energy data processing of power systems and particularly relates to a seasonal Kalman filtering model based power load adaptive prediction method. The method comprises: performing data acquisition on a power load in a production process of a power utilization enterprise, and acquiring a power load curve for recording the power load of the power utilization enterprise in an industrial manufacturing process; performing adaptive periodic identification on the acquired power load curve; then performing fast Fourier transform on power load identification data to obtain a frequency spectrum sequence; and finally, predicting a power utilization mode of the power utilization enterprise by adopting a seasonal Kalman filtering model. For a to-be-predicted power load of each specific power utilization enterprise, the used data is only corresponding data in front-back and previous periods; and the power load adaptive prediction method proposed by the invention is high in prediction accuracy, low in calculation amount and high in antijamming capability.
Description
Technical field
The invention belongs to electric system electric energy data applied technical field, particularly relate to a kind of electric load adaptive forecasting method based on Kalman filter model in season.
Background technology
Kalman filter (Kalmanfiltering) is a kind of linear model based on minimizing covariance evaluated error, and it is simple that it has calculating, the advantage that theoretical foundation is sturdy.Yu Jingwen, Xue Hui, Wen Boying. based on the power quality analysis method survey [J] of Kalman filtering. electric power network technique, 2010,34 (2): 97-103, power quality problem and analyzing detecting method thereof are simply introduced; The major reviews ultimate principle of these 3 kinds of Kalman filterings of conventional Kalman filtering, EKF and Unscented kalman filtering, and the summary of system has been carried out to its application in power quality analysis, the comparative analysis pros and cons of various method.
Ma Jingbo, Yang Honggeng. the application of adaptive Kalman filter in power-system short-term load forecasting [J]. electric power network technique, 2005, the researchers such as 29 (1): 75-79. consider the feature of electric system Self-variation, and the history electricity consumption data of the not synchronization of same date are established load system model, observation model and the system parameter model containing time-varying coefficient.Become Noise statistics extimators during utilization and ART network is carried out to noise covariance, with the load of predictive equation prediction next day, its research shows, the predictive ability becoming the predictive equation of Noise estimation device when considering the self-adaptation of historical data is stronger than general Kalman Prediction model.But its research is that to be based upon every day synchronization power load be under the hypothesis of stationary sequence, when being generalized to the full-time total load of prediction, original hypothesis is not necessarily set up, namely full-time total load might not with load structure stationary sequence of the same period of the last week or the previous moon.
In real production environment, its periodic law is more complicated.Even for the electricity consumption Business entity in the same industry, the cycle of its power load is all different.Therefore, how designing the energy self-adaptation electric load time series forecast model carried out under different cycles is the problem needing to solve.
Summary of the invention
Object of the present invention is the problems referred to above solving prior art, there is provided that a kind of prediction effect is excellent, accuracy rate is high, and based on the electric load adaptive forecasting method of Kalman filter model in season, to achieve these goals, the technical solution used in the present invention is as follows:
Based on an electric load adaptive forecasting method for Kalman filter model in season, it is characterized in that: comprise the following steps:
1), to the electric load in electricity consumption enterprise production process data acquisition is carried out, and acquisition and recording electricity consumption enterprise electric load curve in industrial manufacturing process, the electric load curve collected is carried out self-adaptation Periodic identification.
2), by electric load identification data carry out Fast Fourier Transform (FFT), obtain spectrum sequence s
j, get the maximum sequence subscript of frequency spectrum as cycle T, T equals spectrum sequence s
jcorresponding maximum j, then:
Wherein, h
k∈ (h
0, h
2, h
3..., h
n-1) be the actual value sequence of electric load, j=0,1,2 ..., n-1;
3), adopt Kalman filter model in season predicting with power mode electricity consumption enterprise, Kalman in season carries out forecasting process with power mode and meets:
x
k+1/k＝Ax
k/k-1+G
k[y
k-Hx
k/k-1],
Wherein, x
k+1/kit is the estimated value of the k+1 moment power load based on the k moment; The product of related coefficient and seasonal factor is become, G when A is
kit is prediction gain matrix.
Preferably, the prediction gain matrix of described Kalman Prediction process meets:
G
k＝Ap
k/k-1H
T[Hp
k/k-1H
T+R
k]
1,
Wherein, H is measurement matrix, and it determined by measurement system and measuring method, is time-independent amount, p
k/k-1mean square prediction error equation, R
kit is the variance measuring noise.
Preferably, described season Kalman Prediction process mean square prediction error equation meet:
p
k+1/k＝[A-G
kH]p
k/k-1A
T+Q
k
Q
kit is the variance of process noise.
As further scheme of the present invention, preferably, the coefficient A of described joint Kalman Prediction process meets:
A＝Φ
kΨ
k,
Wherein, Φ
kbe k-1 and the k moment power load between related coefficient; Ψ
kit is the related coefficient in k-T and k moment.
As further scheme of the present invention, preferably, season, Kalman Prediction process was provided with state equation x
kwith measurement equation y
k, and meet:
x
k＝Φ
kx
k-1+Ψ
ku
k+ω
k-1
y
k＝Hx
k+v
k，
Wherein, u
k=x
k-T, by the data in T cycle before the k moment as seasonal factor, ω
k-1represent the process noise in k-1 moment, p (w
k-1) ~ N (0, Q
k-1); v
kmeasure noise, p (v
k) ~ N (0, R
k).
In sum, the present invention is owing to have employed above technical scheme, and the present invention has following remarkable result:
(1), the electric load adaptive forecasting method of invention proposition is without the need to manual intervention, self-adaptation can must calculate sequence to be predicted, and, the electric load adaptive forecasting method predictablity rate that the present invention proposes is high, calculated amount is lower, for the electric load that each specific electricity consumption enterprise is to be predicted, the data of use are only data point corresponding to the previous and last cycle in electric load sequence for future position.
(2), the present invention propose season Kalman filter model by adding seasonal factor, quantized the periodic phenomena often had in real production, thus strengthened the predictive ability of classical kalman filter method.
Accompanying drawing explanation
In order to be illustrated more clearly in example of the present invention or technical scheme of the prior art, introduce doing accompanying drawing required in embodiment or description of the prior art simply below, apparently, accompanying drawing in the following describes is only examples more of the present invention, to those skilled in the art, do not paying under creationary prerequisite, other accompanying drawing can also obtained according to these accompanying drawings.
Fig. 1 is a kind of process flow diagram of electric load adaptive forecasting method based on Kalman filter model in season of the present invention
Fig. 2 be the present invention a kind of based on season Kalman filter model the electro-load forecast value of electric load adaptive forecasting method and actual value comparison diagram.
Fig. 3 be the present invention a kind of based on season Kalman filter model the predicted value of electric load adaptive forecasting method and the scale map of the difference of actual value.
Embodiment
Below in conjunction with the accompanying drawing in example of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Below in conjunction with accompanying drawing 1, a kind of electric load adaptive forecasting method based on Kalman filter model in season, is characterized in that: comprise the following steps:
1), to the electric load in electricity consumption enterprise production process data acquisition is carried out, and acquisition and recording electricity consumption enterprise electric load curve in industrial manufacturing process, the electric load curve collected is carried out self-adaptation Periodic identification; Due to the impact of multiple random factor, as society, society, economy and natural conditions etc., electric load change curve is made to have very complicated non-linear form.But the electric load of same industry always has certain periodicity makes it have predictability.Different cycles and different electrical force profiles forms represent different electric load using forestland.This example have chosen the data of 2014 of somewhere non-ferrous metal metallurgy and calendering processing industry electricity consumption enterprise as training dataset, and the data in January, 2015 to July are as test data set, and data set is as shown in table 1:
Table 1 inputs active energy value
2), by electric load identification data carry out Fast Fourier Transform (FFT), obtain spectrum sequence s
j, then get the maximum sequence subscript of frequency spectrum as cycle T, T equals spectrum sequence s
jcorresponding maximum j, then:
Wherein, h
k∈ (h
0, h
2, h
3..., h
n-1) be the actual value sequence of electric load, j=0,1,2 ..., n-1;
In theory, the cycle with power mode of electricity consumption enterprise can carry out approximate simulation with the recruitment arrangement of this electricity consumption enterprise, but the recruitment arrangement obtaining all electricity consumption enterprises is difficult to realize.Therefore, the present invention proposes to utilize the frequency spectrum of fast fourier transform algorithm to maximize the cycle obtaining electric load time series, i.e. the cycle with power mode of electricity consumption enterprise.In example of the present invention, carry out fast Fourier Spectrum Conversion computation of Period to the data of 2014 of non-ferrous metal metallurgy and calendering processing industry, obtaining its cycle is 106, amounts to about 3 first quarter moons.
3), adopt Kalman filter model in season predicting with power mode electricity consumption enterprise, Kalman in season carries out forecasting process with power mode and meets:
x
k+1/k＝Ax
k/k-1+G
k[y
k-Hx
k/k-1],
Wherein, x
k+1/kit is the estimated value of the k+1 moment power load based on the k moment; The product of related coefficient and seasonal factor is become, G when A is
kprediction gain matrix, the prediction gain matrix G of described Kalman Prediction process
kmeet:
G
k＝Ap
k/k-1H
T[Hp
k/k-1H
T+R
k]
1,
Wherein, H is measurement matrix, and it determined by measurement system and measuring method, is time-independent amount, p
k/k-1mean square prediction error equation, R
kit is the variance measuring noise.
Described season, the mean square prediction error equation of Kalman Prediction process met:
p
k+1/k＝[A-G
kH]p
k/k-1A
T+Q
k,
Q
kbe the variance of process noise, wherein, season, the coefficient A of Kalman Prediction process met: A=Φ
kΨ
k, Φ
kbe k-1 and the k moment power load between related coefficient; Ψ
kit is the related coefficient in k-T and k moment.
In the present invention, season, Kalman Prediction process was provided with state equation x
kwith measurement equation y
k, the data on the same day are estimated, and meet:
x
k＝Φ
kx
k-1+Ψ
ku
k+ω
k-1
y
k＝Hx
k+v
k，
Wherein, u
k=x
k-T, by the data in T cycle before the k moment as impact factor, build season Kalman filtering and carry out; ω
k-1represent the process noise in k-1 moment, p (w
k-1) ~ N (0, Q
k-1); v
kmeasure noise, p (v
k) ~ N (0, R
k).
In this example, cycle T=106, H is initialized as [0.5,0.5]; Process noise variance Q
1be initialized as 4, measure noise variance R
1be initialized as 4, predicting the outcome of illustrating using 1 to 4 January in 2015 as calculated examples is as shown in table 2:
Table 2 predicts the outcome
In table 2, predicted value is the present invention's predicting the outcome to power load on the same day, and estimated value is that the present invention utilizes actual value and predicted value to the estimation of power load on the same day.The electro-load forecast result in January, 2015 to July is as Fig. 2, and as can be seen from the figure, not quite, two curves overlap substantially for prediction curve and actual value deviation.The scale map of the difference of predicted value and actual value is as Fig. 3, figure medial error ratio is that predicted value deducts the difference of actual value divided by actual value, and analysis can obtain, and this example medial error ratio is [-0.5, accounting for 0.5] always predicts 95.75% of number of times, and detailed error rate is as shown in table 3.
Table 3 predicts the outcome errors table
Therefore, the predicated error result from table 3 can prove the Kalman prediction excellent effect in season that the present invention proposes, and restrains respond well.
The foregoing is only the preferred embodiment of invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (5)
1., based on an electric load adaptive forecasting method for Kalman filter model in season, it is characterized in that: comprise the following steps:
1), to the electric load in electricity consumption enterprise production process data acquisition is carried out, and acquisition and recording electricity consumption enterprise electric load curve in industrial manufacturing process, the electric load curve collected is carried out self-adaptation Periodic identification;
2), by electric load identification data carry out Fast Fourier Transform (FFT), obtain spectrum sequence s
j, then get the maximum sequence subscript of frequency spectrum as cycle T, T equals spectrum sequence s
jcorresponding maximum j, then:
Wherein, h
k∈ (h
1, h
2, h
3..., h
n) be the actual value sequence of electric load, k=1,2,3 ..., n,
j＝0,1,2,…,n-1；
3), adopt Kalman filter model in season predicting with power mode electricity consumption enterprise, Kalman in season carries out forecasting process with power mode and meets:
x
k+1/k＝Ax
k/k-1+G
k[y
k-Hx
k/k-1],
Wherein, x
k+1/kbe the estimated value of the k+1 moment power load based on the k moment, become the product of related coefficient and seasonal factor when A is, G
kit is prediction gain matrix.
2. a kind of electric load adaptive forecasting method based on Kalman filter model in season according to claim 1, is characterized in that: the prediction gain matrix of described Kalman Prediction process meets:
G
k＝Ap
k/k-1H
T[Hp
k/k-1H
T+R
k]
1,
Wherein, H is measurement matrix, p
k/k-1mean square prediction error equation, R
kit is the variance measuring noise.
3. a kind of electric load adaptive forecasting method based on Kalman filter model in season according to claim 2, is characterized in that: described season, the mean square prediction error equation of Kalman Prediction process met:
p
k+1/k＝[A-G
kH]p
k/k-1A
T+Q
k，
Q
kit is the variance of process noise.
4. a kind of electric load adaptive forecasting method based on Kalman filter model in season according to claim 1 or 3, is characterized in that: the coefficient A of described joint Kalman Prediction process meets:
A＝Φ
kΨ
k,
Wherein, Φ
kbe k-1 and the k moment power load between related coefficient; Ψ
kit is the related coefficient in k-T and k moment.
5. a kind of electric load adaptive forecasting method based on Kalman filter model in season according to claim 1, is characterized in that: described season, Kalman Prediction process was provided with state equation x
kwith measurement equation y
k, and meet:
x
k＝Φ
kx
k-1+Ψ
ku
k+ω
k-1
y
k＝Hx
k+v
k，
Wherein, u
k=x
k-T, by the data in T cycle before the k moment as seasonal factor, ω
k-1represent the process noise in k-1 moment, p (w
k-1) ~ N (0, Q
k-1), v
kmeasure noise, p (v
k) ~ N (0, R
k).
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108918932A (en) * | 2018-09-11 | 2018-11-30 | 广东石油化工学院 | Power signal adaptive filter method in load decomposition |
| CN112200391A (en) * | 2020-11-17 | 2021-01-08 | 国网陕西省电力公司经济技术研究院 | Power distribution network edge side load prediction method based on k-nearest neighbor mutual information characteristic simplification |
-
2015
- 2015-11-28 CN CN201510846543.XA patent/CN105488590A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108918932A (en) * | 2018-09-11 | 2018-11-30 | 广东石油化工学院 | Power signal adaptive filter method in load decomposition |
| CN108918932B (en) * | 2018-09-11 | 2021-01-15 | 广东石油化工学院 | Adaptive filtering method for power signal in load decomposition |
| CN112200391A (en) * | 2020-11-17 | 2021-01-08 | 国网陕西省电力公司经济技术研究院 | Power distribution network edge side load prediction method based on k-nearest neighbor mutual information characteristic simplification |
| CN112200391B (en) * | 2020-11-17 | 2023-07-04 | 国网陕西省电力公司经济技术研究院 | Power distribution network edge side load prediction method based on k-nearest neighbor mutual information feature simplification |
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Application publication date: 20160413 |