CN113487062A - Power load prediction method based on periodic automatic encoder - Google Patents

Power load prediction method based on periodic automatic encoder Download PDF

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CN113487062A
CN113487062A CN202110597175.5A CN202110597175A CN113487062A CN 113487062 A CN113487062 A CN 113487062A CN 202110597175 A CN202110597175 A CN 202110597175A CN 113487062 A CN113487062 A CN 113487062A
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田英杰
朱征
谢伟
张琪祁
时志雄
苏运
郭乃网
李凡
吴裔
赵莹莹
张菲菲
阮静娴
金妍斐
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China Online Shanghai Energy Internet Research Institute Co ltd
State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention relates to a power load prediction method based on a periodic automatic encoder, which comprises the following steps: collecting power load data of each transformer area to form a historical power time sequence; preprocessing the historical power time sequence, reconstructing the historical power time sequence through a trained periodic automatic encoder, and generating an embedded sequence of the historical power time sequence; and inputting the embedded sequence of the historical power time sequence into a trained predictor to obtain the predicted power load sequence data. Compared with the prior art, the method has the advantages of high prediction precision and the like.

Description

Power load prediction method based on periodic automatic encoder
Technical Field
The invention relates to a power load prediction method, in particular to a power load prediction method based on a periodic automatic encoder.
Background
With the development of economic technology and the improvement of living standard of people, electric energy becomes essential secondary energy in production and life of people, and brings endless convenience to production and life of people.
With the frequent occurrence of extreme weather and the higher and higher requirements of people on living standard and living quality, the demand of people on electricity is increased. With the increase of household electricity consumption of residents, the distribution transformer area is heavily overloaded, especially, the overload condition of the electricity load of the distribution transformer area in winter is serious, the load peak-valley difference is large, especially, the electricity consumption level of the residents reaches the highest peak within one year during the spring festival, and the serious overload condition is easy to occur. The resident is greatly influenced by electricity limit, power failure and tripping in daily life.
The power distribution and utilization global full time sequence load data prediction is a technical means capable of effectively solving the problems and is an important basis for power grid planning, operation and energy-saving management, and the power distribution and utilization global full time sequence load data prediction enables a power system to be prepared in advance by predicting the load of the power system in advance, so that heavy overload of a transformer area is avoided.
However, when individual-level power load prediction is performed, because the global full load of the level has high volatility and uncertainty, the existing prediction method cannot overcome the problem, and the prediction accuracy is not high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a power load prediction method based on a periodic automatic encoder, which has high prediction precision.
The purpose of the invention can be realized by the following technical scheme:
a power load prediction method based on a periodic automatic encoder comprises the following steps:
collecting power load data of each transformer area to form a historical power time sequence;
preprocessing the historical power time sequence, reconstructing the historical power time sequence through a trained periodic automatic encoder, and generating an embedded sequence of the historical power time sequence;
inputting the generated embedded sequence into a trained predictor to obtain predicted power load sequence data;
the historical power time sequence xnIs as follows;
Figure BDA0003091585400000021
wherein N is the number of the station areas, M is the time range value, Qk,tPower load data representing k zones at time t;
the reconstruction process of the periodic automatic encoder comprises the following steps:
z(n,1)＝sigmoid(W1xn+b1)
z(n,2)＝sigmoid(W2z(n,1)+b2)
......
z(n,l)＝sigmoid(Wlz(n,l-1)+bl)
......
z(n,L)＝sigmoid(WLz(n,L-1)+bL)
wherein L is the number of hidden layers of the periodic automatic encoder, and WlAnd blRespectively the weight and the threshold of the hidden layer of the l layer,
Figure BDA0003091585400000022
is the output of the first hidden layer, HlThe number of neurons in the first hidden layer,
Figure BDA0003091585400000023
is the vector space of the first hidden layer, the output z of the last hidden layer(n,L)Namely an embedded sequence generated by a periodic automatic encoder according to a historical power time sequence;
since the individual level power load data has a periodic characteristic, the periodic automatic encoder is used for generating a historical power time sequence xnThe vector space is embedded, the embedded sequence is generated, the generated embedded sequence can capture the inherent periodic characteristics of the power load data, the uncertainty and the volatility of the power load data are filtered, the embedded sequence is input into a predictor, accurate prediction of the power load data of an individual level is achieved, and the prediction accuracy of the power load data is greatly improved.
Further, since the historical power time series share one period auto-encoder, i.e. share the same mapping, to mitigate overfitting, the loss function L of the period auto-encoder is trainedPAEThe root mean square error RMSE is adopted, and the calculation formula is as follows:
Figure BDA0003091585400000031
wherein T is the period of the historical power time sequence, and N is the stationThe number of the zones is such that,
Figure BDA0003091585400000032
for the load value sequence of the nth station at time t,
Figure BDA0003091585400000033
reconstruction of time t for periodic autoencoder output
Figure BDA0003091585400000034
Figure BDA0003091585400000035
The expression of (a) is:
Figure BDA0003091585400000036
wherein, WreconstructAnd breconstructRespectively, the weights and thresholds of the output layers of the periodic auto-encoder.
Further, the formula of the predictor is as follows:
Figure BDA0003091585400000037
Xt,k＝(Weekt,Montht,ADt,Weathert,k,Weatherdif(t,k),Loadbf(t,k))
wherein,
Figure BDA0003091585400000038
for the load prediction value at the kth point of the prediction date t, S is a trained prediction model, thetat,kModel parameters, X, used for predicting the kth time point of day tt,kAs a vector of influencing factors, WeektAs week type influencing factors, MonthtFor month influencing factors, ADtWeather, a factor affecting the overall time trendt,kWeather as a factor influencing meteorological valuesdif(t,k)Relative to the previous dayWeather change influencing factor, Loadbf(t,k)The load value influence factor of the previous day;
the relevant factors influencing the load size are mainly the week type, month, overall time trend, weather value of the day, weather change of the day relative to the previous day, and load value of the previous day.
Further, the prediction model S is a multilayer perceptron MLP, and the reconstruction process is as follows:
h(n,1)＝sigmoid(W'1z(n,L)+b'1)
h(n,2)＝sigmoid(W'2h(n,1)+b'2)
......
h(n,l)＝sigmoid(W'lh(n,l-1)+bl')
......
h(n,L)＝sigmoid(W'L'h(n,L′-1)+b'L')
wherein L' is the number of hidden layers of the prediction model, h(n,l)Is the output of the l-th hidden layer, W'lAnd bl' are the weight and threshold of the l-th hidden layer, respectively.
Further, since the historical power time series share one period of the autoencoder, i.e. share the same mapping, to mitigate overfitting, the loss function L of the prediction model S is trainedMLPThe root mean square error RMSE is adopted, and the calculation formula is as follows:
Figure BDA0003091585400000039
wherein T is the period of the historical power time series, N is the number of the transformer areas,
Figure BDA0003091585400000041
for the embedding sequence of the nth station zone at time t,
Figure BDA0003091585400000042
output for predictive model SReconstruction at time t
Figure BDA0003091585400000043
Figure BDA0003091585400000044
The expression of (a) is:
Figure BDA0003091585400000045
wherein, WforecastAnd bforecastRespectively, the weight and the threshold of the output layer of the prediction model.
Further, the pretreatment process comprises the following steps:
and sequentially performing data cleaning processing, data standardization processing and data noise reduction processing on the historical power time sequence.
Further, the data cleaning process includes:
judging whether the power load data meet an identification inequality, if so, judging that the power load data are normal, otherwise, judging that the power load data are abnormal;
correcting abnormal power load data by using an interpolation method;
the identification inequality is as follows:
P(|Qk,t-μk|＞3σk)≤0.003
wherein, mukIs the mean value, σ, of the power load data of the kth station areakIs the standard deviation of the kth power load data.
Further, the interpolation method comprises:
correcting abnormal power load data by the following formula:
Figure BDA0003091585400000046
wherein,
Figure BDA0003091585400000047
for the load of the abnormal point after interpolation correction, n1Selecting range value n of power load data for time period near abnormal power load data2Selection range value of the area near the area where the abnormal power load data is located, CiIs an interpolation coefficient of the time interval, BjAs interpolation coefficient of the station area, CiAnd BjSatisfies the following conditions:
Figure BDA0003091585400000048
Ciand BjBy minimizing the variance of the corrected power load data
Figure BDA0003091585400000049
To obtain.
Further, the data normalization processing procedure includes:
the method comprises the following steps of normalizing the power load data by using a dispersion normalization formula, wherein the dispersion normalization formula is as follows:
Figure BDA00030915854000000410
wherein, Q'k,tNamely, the normalized power load data at the time point of the k station zone t.
Further, the data denoising processing process includes:
and denoising the power load data by adopting wavelet transformation.
Compared with the prior art, the invention has the following beneficial effects:
the invention collects the power load data of each station area to form a historical power time sequence, preprocesses the historical power time sequence, reconstructs the historical power time sequence through a trained period automatic encoder to generate an embedded sequence of the historical power time sequence, inputs the generated embedded sequence into a trained predictor to obtain predicted power load sequence data and realizes the prediction of power load data of individual levels, because the power load data of the individual levels have obvious period characteristics, the period automatic encoder embeds the historical power time sequence into a vector space and generates the embedded sequence, the generated embedded sequence can capture the inherent period characteristics of the power load data and filter the uncertainty and the fluctuation of the power load data, and then inputs the embedded sequence into the predictor, the predictor is not easy to learn the characteristics of random fluctuating noise in the historical power time sequence, the training process is more stable and not easy to over-fit, and the prediction precision is greatly improved.
Drawings
FIG. 1 is a schematic diagram of a periodic auto-encoder;
FIG. 2 is a graph of the change in RMSE for the test set at 48 points predicted at 48 points;
FIG. 3 is a graph of the change in RMSE for the test set at 96 points predicted 48 points;
FIG. 4 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
A power load prediction method based on a periodic automatic encoder, as shown in fig. 4, includes:
1) collecting power load data of each transformer area to form a historical power time sequence;
2) preprocessing the historical power time sequence, reconstructing the historical power time sequence through a trained periodic automatic encoder PAE, and generating an embedded sequence of the historical power time sequence;
3) inputting the generated embedded sequence into a trained predictor to obtain predicted power load sequence data;
historical power time series xnIs as follows;
Figure BDA0003091585400000061
wherein N is the number of the station areas, M is the time range value, Qk,tPower load data representing k zones at time t;
as shown in fig. 1, the reconstruction process of the periodic automatic encoder is as follows:
z(n,1)＝sigmoid(W1xn+b1)
z(n,2)＝sigmoid(W2z(n,1)+b2)
......
z(n,l)＝sigmoid(Wlz(n,l-1)+bl)
......
z(n,L)＝sigmoid(WLz(n,L-1)+bL)
wherein L is the number of hidden layers of the periodic automatic encoder, and WlAnd blRespectively the weight and the threshold of the hidden layer of the l layer,
Figure BDA0003091585400000062
is the output of the first hidden layer, HlThe number of neurons in the first hidden layer,
Figure BDA0003091585400000063
is the vector space of the first hidden layer, the output z of the last hidden layer(n,L)Namely an embedded sequence generated by a periodic automatic encoder according to a historical power time sequence;
since the power load data has a periodic characteristic, the periodic autoencoder will history the power time series xnThe vector space is embedded, the embedded sequence is generated, the generated embedded sequence can capture the inherent periodic characteristics of the power load data, the uncertainty and the volatility of the power load data are filtered, the embedded sequence is input into a predictor, accurate prediction of the power load data of an individual level is achieved, and the prediction accuracy of the power load data is greatly improved.
One cycle automatic due to historical power time series sharingEncoders, i.e. sharing the same mapping, train the loss function L of periodic auto-encoders to mitigate overfittingPAEThe root mean square error RMSE is adopted, and the calculation formula is as follows:
Figure BDA0003091585400000064
wherein T is the period of the historical power time series, N is the number of the transformer areas,
Figure BDA0003091585400000065
for the load value sequence of the nth station at time t,
Figure BDA0003091585400000066
reconstruction of time t for periodic autoencoder output
Figure BDA0003091585400000067
Figure BDA0003091585400000068
The expression of (a) is:
Figure BDA0003091585400000069
wherein, WreconstructAnd breconstructRespectively, the weights and thresholds of the output layers of the periodic auto-encoder.
The formula of the predictor is:
Figure BDA0003091585400000071
Xt,k＝(Weekt,Montht,ADt,Weathert,k,Weatherdif(t,k),Loadbf(t,k))
wherein,
Figure BDA0003091585400000072
for the load prediction value at the kth point of the prediction date t, S is a trained prediction model, thetat,kModel parameters, X, used for predicting the kth time point of day tt,kAs a vector of influencing factors, WeektAs week type influencing factors, MonthtFor month influencing factors, ADtWeather, a factor affecting the overall time trendt,kWeather as a factor influencing meteorological valuesdif(t,k)Load as a factor influencing weather changes from the previous daybf(t,k)The load value influence factor of the previous day;
the relevant factors influencing the load size are mainly the week type, month, overall time trend, weather value of the day, weather change of the day relative to the previous day, and load value of the previous day.
The prediction model S is a multilayer perceptron MLP, and the reconstruction process is as follows:
h(n,1)＝sigmoid(W'1z(n,L)+b'1)
h(n,2)＝sigmoid(W'2h(n,1)+b'2)
......
h(n,l)＝sigmoid(W'lh(n,l-1)+bl')
......
h(n,L)＝sigmoid(W'L'h(n,L′-1)+b'L')
wherein L' is the number of hidden layers of the prediction model, h(n,l)Is the output of the l-th hidden layer, W'lAnd bl' are the weight and threshold of the l-th hidden layer, respectively.
Since the historical power time series share one period of the automatic encoder, i.e. share the same mapping, to mitigate overfitting, the loss function L of the prediction model S is trainedMLPThe root mean square error RMSE is adopted, and the calculation formula is as follows:
Figure BDA0003091585400000073
wherein T isThe period of the historical power time series, N being the number of zones,
Figure BDA0003091585400000074
for the embedding sequence of the nth station zone at time t,
Figure BDA0003091585400000075
reconstruction of time t for prediction model S output
Figure BDA0003091585400000076
Figure BDA0003091585400000077
The expression of (a) is:
Figure BDA0003091585400000078
wherein, WforecastAnd bforecastRespectively, the weight and the threshold of the output layer of the prediction model.
The pretreatment process comprises the following steps:
and sequentially performing data cleaning processing, data standardization processing and data noise reduction processing on the historical power time sequence.
The data cleaning processing process comprises the following steps:
judging whether the power load data meet an identification inequality, if so, judging that the power load data are normal, otherwise, judging that the power load data are abnormal;
correcting abnormal power load data by using an interpolation method;
assuming that the historical power time series obeys normal distribution, the identification inequality is as follows:
P(|Qk,t-μk|＞3σk)≤0.003
wherein, mukIs the mean value, σ, of the power load data of the kth station areakThe standard deviation of the power load data of the kth station area is shown;
abnormal values and missing values in the historical power time series may be identified and corrected by the data cleansing process.
The interpolation method comprises the following steps:
correcting abnormal power load data by the following formula:
Figure BDA0003091585400000081
wherein,
Figure BDA0003091585400000082
for the load of the abnormal point after interpolation correction, n1Selecting range value n of power load data for time period near abnormal power load data2Selection range value of the area near the area where the abnormal power load data is located, CiIs an interpolation coefficient of the time interval, BjAs interpolation coefficient of the station area, CiAnd BjSatisfies the following conditions:
Figure BDA0003091585400000083
Ciand BjBy minimizing the variance of the corrected power load data
Figure BDA0003091585400000084
To obtain.
The data standardization processing process comprises the following steps:
the method comprises the following steps of (1) carrying out standardization processing on power load data by using a dispersion standardization formula, wherein the dispersion standardization formula is as follows:
Figure BDA0003091585400000085
wherein Q isk′,tNamely, the normalized power load data at the time point of the k station zone t.
The data denoising processing process comprises the following steps:
and denoising the power load data by adopting wavelet transformation.
Experiment one
The original historical power time series and the embedded series produced by the periodic automatic encoder are respectively input into a predictor to carry out a comparison experiment, in the experiment, for each household user, 48 points of the day 1 are firstly used for predicting 48 points of the day 2, and then 96 points of the day 1 and the day 2 are used for predicting 48 points of the day 3, and the results of the experiment are shown in the following table:
table 1 experimental results of experiment one
Figure BDA0003091585400000091
Wherein RMSE is the root mean square error, MAPE is the mean absolute percentage error, MAPE is defined as:
Figure BDA0003091585400000092
wherein, YtAnd
Figure BDA0003091585400000093
respectively the actual value and the predicted value at the time t, n3Representing a total number of predicted power load data;
the graph of the change of the RMSE of the test set at 48 points is predicted as shown in fig. 2, the graph of the change of the RMSE of the test set at 48 points is predicted as shown in fig. 3 at 96 points, the horizontal axes of fig. 2 and 3 are the number of training rounds, and the vertical axis is the value of the RMSE, as can be seen from table 1, fig. 2 and fig. 3, the embedded sequence generated by the periodic automatic encoder can filter the uncertainty and the fluctuation of the power load data, therefore, compared with the original historical power time sequence, the embedded sequence generated by the periodic automatic encoder is used for predicting the future power load data, the predictor is not easy to learn the characteristics of the noise which fluctuates randomly in the historical power time sequence, the training process is more stable and not easy to over-fit, and the prediction accuracy is greatly improved.
Experiment two
In order to show that the embedded sequence generated by the periodic automatic encoder can capture the inherent periodic characteristics of the global full-consumption power distribution load and further improve the prediction precision,
the prediction was performed using a periodic autoencoder and a traditional embedding method, respectively, and comparative experiments were performed on the EDRP dataset:
the period automatic encoder embeds the historical power time sequence into a vector space according to periods, the traditional embedding method does not generate the embedded sequence according to periods, and other influence factors are kept consistent;
for each family user, firstly, a periodic automatic encoder uses 48 points on the 1 st day to predict 48 points on the 2 nd day, and the traditional embedding method uses the first 10 points to predict 48 points; the periodic autoencoder then used the 96 points for day 1 and day 2 of the user to predict 48 points for day 3, and the conventional embedding method used the first 100 points to predict the last 48 points, with the results of the experiment shown in the table below.
TABLE 2 Experimental results of experiment two
Figure BDA0003091585400000101
It can be seen that the conventional embedding method does not capture the cycle characteristics which are important for power load prediction, and although the historical power utilization information is increased, the prediction accuracy is not high, compared with the conventional embedding method, the embedded sequence generated by the cycle automatic encoder can capture the cycle characteristics inherent to the power load, and further can obtain higher accuracy.
According to the power load prediction method based on the periodic automatic encoder, the periodic automatic encoder PAE is used for embedding the power time sequence into the vector space according to the period, the embedded sequence generated by the PAE can capture the inherent periodic characteristics of the power load, and the prediction precision is greatly improved.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A power load prediction method based on a periodic automatic encoder is characterized by comprising the following steps:
collecting power load data of each transformer area to form a historical power time sequence;
preprocessing the historical power time sequence, reconstructing the historical power time sequence through a trained periodic automatic encoder, and generating an embedded sequence of the historical power time sequence;
inputting the embedded sequence of the historical power time sequence into a trained predictor to obtain predicted power load sequence data;
the historical power time sequence is as follows;
Figure FDA0003091585390000011
wherein N is the number of the station areas, M is the time range value, Qk,tPower load data representing k zones at time t;
the reconstruction process of the periodic automatic encoder comprises the following steps:
z(n,1)＝sigmoid(W1xn+b1)
z(n,2)＝sigmoid(W2z(n,1)+b2)
......
z(n,l)＝sigmoid(Wlz(n,l-1)+bl)
......
z(n,L)＝sigmoid(WLz(n,L-1)+bL)
wherein x isnFor historical power time series, L is the number of hidden layers of the periodic automatic encoder, WlAnd blRespectively the weight and the threshold of the hidden layer of the l layer,
Figure FDA0003091585390000012
is the output of the first hidden layer, HlThe number of neurons in the first hidden layer and the output z of the last hidden layer(n,L)I.e., an embedded sequence generated by a periodic autoencoder from a historical power time sequence.
2. The method according to claim 1, wherein the loss function L of the periodic automatic encoder is trainedPAEThe root mean square error RMSE is adopted, and the calculation formula is as follows:
Figure FDA0003091585390000021
wherein T is the period of the historical power time series, N is the number of the transformer areas,
Figure FDA0003091585390000022
for the load value sequence of the nth station at time t,
Figure FDA0003091585390000023
reconstruction of time t for periodic autoencoder output
Figure FDA0003091585390000024
The expression of (a) is:
Figure FDA0003091585390000025
wherein, WreconstructAnd breconstructRespectively, the weights and thresholds of the output layers of the periodic auto-encoder.
3. The method of claim 1, wherein the predictor has the formula:
Figure FDA0003091585390000026
Xt,k＝(Weekt,Montht,ADt,Weathert,k,Weatherdif(t,k),Loadbf(t,k))
wherein,
Figure FDA0003091585390000027
for the load prediction value at the kth point of the prediction date t, S is a trained prediction model, thetat,kModel parameters, X, used for predicting the kth time point of day tt,kAs a vector of influencing factors, WeektAs week type influencing factors, MonthtFor month influencing factors, ADtWeather, a factor affecting the overall time trendt,kWeather as a factor influencing meteorological valuesdif(t,k)Load as a factor influencing weather changes from the previous daybf(t,k)Is the influence factor of the load value of the previous day.
4. The method according to claim 3, wherein the prediction model S is a multi-layer perceptron MLP, and the reconstruction process is as follows:
h(n,1)＝sigmoid(W'1z(n,L)+b'1)
h(n,2)＝sigmoid(W'2h(n,1)+b'2)
......
h(n,l)＝sigmoid(W'lh(n,l-1)+b'l)
......
h(n,L)＝sigmoid(W'L'h(n,L′-1)+b'L')
wherein L' is the number of hidden layers of the prediction model, h(n,l)Is the output of the l-th hidden layer, W'lAnd b'lThe weight and the threshold of the hidden layer of the l-th layer are respectively.
5. The method according to claim 3, wherein the loss function L of the prediction model S is trainedMLPThe root mean square error RMSE is adopted, and the calculation formula is as follows:
Figure FDA0003091585390000028
wherein T is the period of the historical power time series, N is the number of the transformer areas,
Figure FDA0003091585390000029
for the embedding sequence of the nth station zone at time t,
Figure FDA0003091585390000031
reconstruction of time t for prediction model S output
Figure FDA0003091585390000032
The expression of (a) is:
Figure FDA0003091585390000033
wherein, WforecastAnd bforecastRespectively, the weight and the threshold of the output layer of the prediction model.
6. The method of claim 1, wherein the preprocessing comprises:
and sequentially performing data cleaning processing, data standardization processing and data noise reduction processing on the historical power time sequence.
7. The method of claim 6, wherein the data cleansing process comprises:
judging whether the power load data meet an identification inequality, if so, judging that the power load data are normal, otherwise, judging that the power load data are abnormal;
correcting abnormal power load data by using an interpolation method;
the identification inequality is as follows:
P(|Qk,t-μk|＞3σk)≤0.003
wherein, mukIs the mean value, σ, of the power load data of the kth station areakIs the standard deviation of the kth power load data.
8. The method of claim 7, wherein the interpolation comprises:
correcting abnormal power load data by the following formula:
Figure FDA0003091585390000034
wherein,
Figure FDA0003091585390000035
for the load of the abnormal point after interpolation correction, n1Selecting range value n of power load data for time period near abnormal power load data2Selection range value of the area near the area where the abnormal power load data is located, CiIs an interpolation coefficient of the time interval, BjAs interpolation coefficient of the station area, CiAnd BjSatisfies the following conditions:
Figure FDA0003091585390000036
region load sequence variance with coefficient modified by minimization
Figure FDA0003091585390000037
To obtain.
9. The method of claim 6, wherein the data normalization process comprises:
the method comprises the following steps of normalizing the power load data by using a dispersion normalization formula, wherein the dispersion normalization formula is as follows:
Figure FDA0003091585390000041
wherein, Q'k,tNamely, the normalized power load data at the time point of the k station zone t.
10. The method of claim 6, wherein the data de-noising process comprises:
and denoising the power load data by adopting wavelet transformation.
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