CN112488396A - Wavelet transform-based electric power load prediction method of Holt-Winters and LSTM combined model - Google Patents

Wavelet transform-based electric power load prediction method of Holt-Winters and LSTM combined model Download PDF

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CN112488396A
CN112488396A CN202011387066.2A CN202011387066A CN112488396A CN 112488396 A CN112488396 A CN 112488396A CN 202011387066 A CN202011387066 A CN 202011387066A CN 112488396 A CN112488396 A CN 112488396A
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杨首晖
陈传彬
王良缘
郑建辉
王雪晶
吴元林
陈静
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Fuzhou University
State Grid Fujian Electric Power Co Ltd
Trading Center of State Grid Fujian Electric Power Co Ltd
Great Power Science and Technology Co of State Grid Information and Telecommunication Co Ltd
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State Grid Fujian Electric Power Co Ltd
Trading Center of State Grid Fujian Electric Power Co Ltd
Great Power Science and Technology Co of State Grid Information and Telecommunication Co Ltd
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Abstract

The invention relates to a wavelet transform-based power load prediction method of a Holt-Winters and LSTM combined model, which comprises the following steps of: step S1, acquiring the actual energy consumption data of the power consumer load and preprocessing the data; step S2, removing potential noise in the load data by wavelet denoising with a preset threshold according to the preprocessed load data, and performing discrete wavelet decomposition; step S3, constructing and training a Holt-Winters model according to the decomposed wavelet coefficients of each layer as training samples; step S4, according to the preprocessed load data, a deep learning framework is adopted to build a deep long-term and short-term memory network model; step S5: combining the Holt-Winters model and the depth long-time memory network model, and calculating the weight of each model in the combined model; and step S6, carrying out weighted average on outputs of the Holt-Winters model and the depth long-time memory network model according to the obtained weights to obtain a final prediction curve. The invention effectively improves the reliability of power load prediction.

Description

Wavelet transform-based electric power load prediction method of Holt-Winters and LSTM combined model
Technical Field
The invention relates to the field of power load prediction, in particular to a power load prediction method based on a Holt-Winters and LSTM combined model of wavelet transformation.
Background
The load prediction plays a very important role in the economic and reliable operation of a power grid system and also plays an important role in the electric power trading market. For power generation enterprises, accurate load prediction is beneficial to reasonably arranging the operation mode of a power grid and formulating reasonable power supply construction planning, and for power selling companies, the accurate load prediction is beneficial to realizing maximized profit in electric quantity transaction of a spot market, and meanwhile, value-added services such as energy conservation and the like can be made for enterprise users. At the present stage, the number of electricity selling companies in China increases year by year, at the moment, the strength of the load forecasting capability determines the competitive power of the electricity selling companies in the market, and because the electricity selling companies cannot give reasonable and effective quotations in the spot market without accurate load forecasting results, high deviation cost is easily caused, so the load forecasting capability is very important for the current electricity selling companies.
Disclosure of Invention
In view of this, the present invention provides a power load prediction method based on a wavelet transform Holt-Winters and LSTM combined model, which combines Holt-Winters and LSTM to effectively improve reliability of power load prediction.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power load prediction method based on a Holt-Winters and LSTM combined model of wavelet transformation comprises the following steps:
step S1, acquiring the actual energy consumption data of the power consumer load and preprocessing the data;
step S2, removing potential noise in the load data by wavelet denoising with a preset threshold according to the preprocessed load data, and performing discrete wavelet decomposition;
step S3, constructing and training a Holt-Winters model according to the decomposed wavelet coefficients of each layer as training samples;
step S4, according to the preprocessed load data, a deep learning framework is adopted to build a deep long-term and short-term memory network model;
step S5: combining the Holt-Winters model and the depth long-time memory network model, and calculating the weight of each model in the combined model;
and step S6, carrying out weighted average on outputs of the Holt-Winters model and the depth long-time memory network model according to the obtained weights to obtain a final prediction curve.
Further, the preprocessing comprises elimination processing and normalization processing of negative values, null values, zero values and abnormal values.
Further, the normalization processing specifically includes:
Figure BDA0002809991960000021
in the formula, PjIs the load, P, at the j-th point of a certain user on a certain dayj.minAnd Pj.maxThe load is the minimum value and the maximum value of the load of the user at the jth point of the day respectively; pj' is a value normalized by the load at the j point of a certain day of a certain user.
Further, the step S3 is specifically:
and step S31, decomposing the discrete signal to obtain a corresponding wavelet coefficient, wherein the decomposition formula is as follows:
Figure BDA0002809991960000022
constructing a new discrete signal according to the wavelet coefficient, wherein a reconstruction formula is shown as follows:
Figure BDA0002809991960000031
the formula can be abbreviated as:
Figure BDA0002809991960000032
in the formula (I), the compound is shown in the specification,
Figure BDA0002809991960000033
is the wavelet coefficient of the j-th layer, k is the position index,
Figure BDA0002809991960000034
for wavelet functions, x (t) is the original signal, Aj(t) For low frequency components of the original signal at layer j, and Dj(t) is a high frequency component of the j-th layer;
step S32, the Holt-Winters model is composed of three exponential smoothing equations and a prediction equation, and the specific formula is as follows:
si＝α(xi-pi-l)+(1+α)(si-1+ti-1)
ti＝β(si-si-1)+(1-β)ti-1
pi＝γ(xi-si)+(1-γ)pi-l
xi+h＝si+hti+pi-k+h
in the formula, si、ti、piRespectively representing the horizontal characteristic, the trend characteristic and the seasonal characteristic of the original data at the ith time point, respectively corresponding to damping factors of three components, wherein the damping factors are respectively alpha, beta and gamma and range from 0 to 1, and xiThe actual value of the data at the ith time point, h is the step size from the observation point to the prediction point, and l is the period of the seasonal component;
further, the Holt-Winters model optimizes the parameters of the model by using a PSO algorithm in the training process, specifically, the PSO algorithm is used for searching the optimal value of the seasonal parameter l in the Holt-Winters model, the fitness function of the algorithm is the average absolute error between the predicted value of the Holt-Winters model and the true value of the test sample, and the calculation formula is as follows:
Figure BDA0002809991960000035
in the formula, yiRepresents the actual value of the sample sequence, and
Figure BDA0002809991960000036
it is the predicted value of the sequence.
Further, the step S5 is specifically:
step S51, determining two prediction modes according to the fitting effect of the two modelsWeight of type in combinatorial model ωiThen the formula of its combined prediction model can be recorded as:
Figure BDA0002809991960000041
in the formula: f. oftCombining the predicted values of the prediction models for the time t; f. ofitThe predicted value of the ith prediction model at the moment t;
and step S52, according to the optimization theory, defining the loss function as the minimum sum of squares of the prediction error, wherein the formula is as follows:
Figure BDA0002809991960000042
and satisfies the condition:
Figure BDA0002809991960000043
in the formula, etCombining predicted errors for time t; y istIs an observed value; e is the sum of squares of errors;
step S53, respectively setting the weights of the Holt-Winters model and the LSTM model as omega1、ω2Predicted value is Y1、Y2The final predicted value of the two models is YcThen the formula can be written as:
Yc＝ω1Y1+ω2Y2
obtaining the variance and covariance of the errors of the two models through the prediction errors of the two models, and setting the variance and covariance as sigma11，σ12，σ22And finally calculating the weight coefficients of the two prediction models as follows:
Figure BDA0002809991960000044
Figure BDA0002809991960000051
compared with the prior art, the invention has the following beneficial effects:
the method combines Holt-Winters and LSTM, and effectively improves the reliability of power load prediction.
Drawings
FIG. 1 is a functional block diagram of the method of the present invention;
FIG. 2 is a diagram of a seasonal Holt-Winters principle based on wavelet transforms in an embodiment of the present invention;
FIG. 3 is a diagram of the structure of an LSTM cell unit in accordance with an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a power load prediction method based on a wavelet transform Holt-Winters and LSTM combined model, comprising the following steps:
step S1, acquiring the actual energy consumption data of the power consumer load and preprocessing the data;
in this embodiment, the actual energy consumption data of the power consumer load with a period of one year and a time resolution of 15min is acquired, and since the load data in the database is the result of multiple transmissions of the metering meter, unexpected factors may occur in each transmission process to affect the accuracy of the final data, and in order to reduce the influence of these data on the model predictive performance, the preprocessing operation needs to be performed on the original data before the experiment is performed. Including but not limited to negative, null, zero, outlier processing.
In order to eliminate the difference of dimension and magnitude between the variables, the preprocessed load data is normalized. The normalization method adopts a maximum and minimum normalization method, normalizes all the user electricity load data to a [0,1] interval, and only reserves the electricity utilization habits and characteristics of a user electricity load curve so as to find out the similarity between the user electricity utilization modes. The normalization companies are as follows:
Figure BDA0002809991960000061
in the formula, PjIs the load, P, at the j-th point of a certain user on a certain dayj.minAnd Pj.maxRespectively, the minimum value and the maximum value of the load of the user at 96 points of the day. Pj' is a value normalized by the load at the j point of a certain day of a certain user.
Step S2, removing potential noise in the load data by wavelet denoising with a preset threshold according to the preprocessed load data, and performing discrete wavelet decomposition;
in this embodiment, the normalized load data is drawn as a daily electricity load curve, and because the daily load curve obtained from the raw data is relatively tortuous and has more burrs, in order to enable the model to better fit the input load sequence during training to find the optimal parameters, the wavelet denoising with a proper threshold value is adopted to eliminate the potential noise in the load data, and the discrete wavelet decomposition is performed on the load data subjected to the wavelet denoising. As shown in fig. 2, Daubechies8 wavelet (db 8 for short) is taken as a mother wavelet, the number of layers of decomposition is selected to be 3, and the curve is smoothed to some extent, so that the characteristics of the curve are retained while noise data are filtered.
Step S3, constructing and training a Holt-Winters model according to the decomposed wavelet coefficients of each layer as training samples; the method specifically comprises the following steps:
and step S31, decomposing the discrete signal to obtain a corresponding wavelet coefficient, wherein the decomposition formula is as follows:
Figure BDA0002809991960000071
constructing a new discrete signal according to the wavelet coefficient, wherein a reconstruction formula is shown as follows:
Figure BDA0002809991960000072
the formula can be abbreviated as:
Figure BDA0002809991960000073
in the formula (I), the compound is shown in the specification,
Figure BDA0002809991960000074
is the wavelet coefficient of the j-th layer, k is the position index,
Figure BDA0002809991960000075
for wavelet functions, x (t) is the original signal, Aj(t) is the low frequency component of the original signal at layer j, and Dj(t) is a high frequency component of the j-th layer;
and step S32, the power load is obviously influenced by meteorological factors such as ambient temperature, humidity, sunshine, rainfall and the like in different seasons, so that the load data has a time sequence with obvious seasonal characteristics. The Holt-Winters model is a method which is based on an exponential smoothing method and specially predicts a time series with seasonal characteristics. The core idea of the exponential smoothing method is to predict the data size of an observation point by weighted summation of historical data, and the weight size of each historical data point is related to the distance of the point from the predicted point, and the longer the distance is, the smaller the weight is. The Holt-Winters model adds a seasonal component to an original smoothing formula, namely the Holt-Winters model not only performs weighted summation on historical data, but also performs weighted summation on trends and seasonal characteristics of the data. The Holt-Winters model consists of three exponential smoothing equations and a prediction equation, and the specific formula is as follows:
si＝α(xi-pi-l)+(1+α)(si-1+ti-1)
ti＝β(si-si-1)+(1-β)ti-1
pi＝γ(xi-si)+(1-γ)pi-l
xi+h＝si+hti+pi-k+h
in the formula, si、ti、piRespectively representing the horizontal characteristic, the trend characteristic and the seasonal characteristic of the original data at the ith time point, respectively corresponding to damping factors of three components, wherein the damping factors are respectively alpha, beta and gamma and range from 0 to 1, and xiIs the actual value of the data at the ith time point, h is the step size from the observation point to the prediction point, and l is the period of the seasonal component.
Preferably, the Holt-Winters model optimizes the parameters of the model by using a PSO algorithm in the training process, specifically, the PSO algorithm is used for searching the optimal value of the seasonal parameter l in the Holt-Winters model, the fitness function of the algorithm is the average absolute error between the predicted value of the Holt-Winters model and the true value of the test sample, and the calculation formula is as follows:
Figure BDA0002809991960000081
in the formula, yiRepresents the actual value of the sample sequence, and
Figure BDA0002809991960000082
it is the predicted value of the sequence.
Step S4, according to the preprocessed load data, a deep learning framework is adopted to build a deep long-term and short-term memory network model;
in this example, the cell unit structure of LSTM is as shown in fig. 3, and the cell unit of LSTM has three gates, i.e., Input Gate, Forget Gate, and Output Gate. The input gate is used for controlling information input, the forgetting gate is used for controlling the retention of cell historical state information, and the output gate is used for controlling information output. Activating a function to enable the output value of the forgetting gate to be between [0 and 1], and when the output of the forgetting gate is 0, the function shows that all information in the last state is discarded; when 1, all the information indicating the previous state is retained. The calculation process is as follows:
ft＝σ(Wf·[ht-1,xt]+bf)
it＝σ(Wi·[ht-1,xt]+bi)
Figure BDA0002809991960000083
Figure BDA0002809991960000084
ot＝σ(Wo·[ht-1,xt]+bo)
ht＝ot·tanh(Ct)
in the formula, xtAs an input vector of the model, htFor each LSTM cell output vector, itTo the output of the input gate, ftFor forgetting the output of the gate, CtCell state at the present time t, otIs the output of the output gate, where W and b are parameter matrices.
In the model building process, the output mode of the model is designed to enable the load prediction model to be direct multi-step prediction, namely, the power user load curves of a plurality of time periods in the future can be directly predicted.
Step S5: combining the Holt-Winters model and the depth long-time memory network model, and calculating the weight of each model in the combined model; the method specifically comprises the following steps:
step S51, determining the weight omega of the two prediction models in the combined model according to the fitting effect of the two modelsiThen the formula of its combined prediction model can be recorded as:
Figure BDA0002809991960000091
in the formula: f. oftCombining the predicted values of the prediction models for the time t; f. ofitThe predicted value of the ith prediction model at the moment t;
and step S52, according to the optimization theory, defining the loss function as the minimum sum of squares of the prediction error, wherein the formula is as follows:
Figure BDA0002809991960000092
and satisfies the condition:
Figure BDA0002809991960000093
in the formula, etCombining predicted errors for time t; y istIs an observed value; e is the sum of squares of errors;
step S53, respectively setting the weights of the Holt-Winters model and the LSTM model as omega1、ω2Predicted value is Y1、Y2The final predicted value of the two models is YcThen the formula can be written as:
Yc＝ω1Y1+ω2Y2
obtaining the variance and covariance of the errors of the two models through the prediction errors of the two models, and setting the variance and covariance as sigma11，σ12，σ22And finally calculating the weight coefficients of the two prediction models as follows:
Figure BDA0002809991960000101
Figure BDA0002809991960000102
and step S6, carrying out weighted average on outputs of the Holt-Winters model and the depth long-time memory network model according to the obtained weights to obtain a final prediction curve.
In this embodiment, the raw data is divided into training data and test data to determine the accuracy of the algorithm's predictions. 80% were randomly selected as training samples and 20% as test samples. And (5) carrying out performance test by using the trained model to obtain the performance evaluation index of the trained model. The performance indexes of the model are phase-to-fraction error MAPE and root mean square error RMSE values, the smaller the index value is, the higher the prediction precision is, and the calculation formula of the amphoteric performance index is as follows:
Figure BDA0002809991960000103
Figure BDA0002809991960000104
the above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (6)

1. A power load prediction method based on a Holt-Winters and LSTM combined model of wavelet transformation is characterized by comprising the following steps:
step S1, acquiring the actual energy consumption data of the power consumer load and preprocessing the data;
step S2, removing potential noise in the load data by wavelet denoising with a preset threshold according to the preprocessed load data, and performing discrete wavelet decomposition;
step S3, constructing and training a Holt-Winters model according to the decomposed wavelet coefficients of each layer as training samples;
step S4, according to the preprocessed load data, a deep learning framework is adopted to build a deep long-term and short-term memory network model;
step S5: combining the Holt-Winters model and the depth long-time memory network model, and calculating the weight of each model in the combined model;
and step S6, carrying out weighted average on outputs of the Holt-Winters model and the depth long-time memory network model according to the obtained weights to obtain a final prediction curve.
2. The wavelet transform-based electric power load prediction method of the Holt-Winters and LSTM combined model, according to claim 1, characterized in that the preprocessing includes elimination processing and normalization processing for negative values, null values, zero values, outliers.
3. The wavelet transform-based electric power load prediction method of the Holt-Winters and LSTM combined model according to claim 2, wherein the normalization process specifically comprises:
Figure FDA0002809991950000011
in the formula, PjIs the load, P, at the j-th point of a certain user on a certain dayj.minAnd Pj.maxThe load is the minimum value and the maximum value of the load of the user at the jth point of the day respectively; pj' is a value normalized by the load at the j point of a certain day of a certain user.
4. The method for predicting power load based on the wavelet transform Holt-Winters and LSTM combined model as claimed in claim 1, wherein said step S3 is specifically:
and step S31, decomposing the discrete signal to obtain a corresponding wavelet coefficient, wherein the decomposition formula is as follows:
Figure FDA0002809991950000021
constructing a new discrete signal according to the wavelet coefficient, wherein a reconstruction formula is shown as follows:
Figure FDA0002809991950000022
the formula can be abbreviated as:
Figure FDA0002809991950000023
in the formula (I), the compound is shown in the specification,
Figure FDA0002809991950000024
is the wavelet coefficient of the j-th layer, k is the position index,
Figure FDA0002809991950000025
for wavelet functions, x (t) is the original signal, Aj(t) is the low frequency component of the original signal at layer j, and Dj(t) is a high frequency component of the j-th layer;
step S32, the Holt-Winters model is composed of three exponential smoothing equations and a prediction equation, and the specific formula is as follows:
si＝α(xi-pi-l)+(1+α)(si-1+ti-1)
ti＝β(si-si-1)+(1-β)ti-1
pi＝γ(xi-si)+(1-γ)pi-l
xi+h＝si+hti+pi-k+h
in the formula, si、ti、piRespectively representing the horizontal characteristic, the trend characteristic and the seasonal characteristic of the original data at the ith time point, respectively corresponding to damping factors of three components, wherein the damping factors are respectively alpha, beta and gamma and range from 0 to 1, and xiIs the actual value of the data at the ith time point, h is the step size from the observation point to the prediction point, and l is the period of the seasonal component.
5. The wavelet transform-based electric power load prediction method for the Holt-Winters and LSTM combined model is characterized in that the Holt-Winters model uses a PSO algorithm to optimize parameters of the model in a training process, specifically, the PSO algorithm is used for finding an optimal value of a seasonal parameter l in the Holt-Winters model, a fitness function of the algorithm is an average absolute error between a predicted value of the Holt-Winters model and a true value of a test sample, and a calculation formula is as follows:
Figure FDA0002809991950000031
in the formula, yiRepresents the actual value of the sample sequence, and
Figure FDA0002809991950000032
it is the predicted value of the sequence.
6. The method for predicting power load based on the wavelet transform Holt-Winters and LSTM combined model as claimed in claim 1, wherein said step S5 is specifically:
step S51, determining the weight omega of the two prediction models in the combined model according to the fitting effect of the two modelsiThen the formula of its combined prediction model can be recorded as:
Figure FDA0002809991950000033
in the formula: f. oftCombining the predicted values of the prediction models for the time t; f. ofitThe predicted value of the ith prediction model at the moment t;
and step S52, according to the optimization theory, defining the loss function as the minimum sum of squares of the prediction error, wherein the formula is as follows:
Figure FDA0002809991950000034
and satisfies the condition:
Figure FDA0002809991950000035
in the formula, etCombining predicted errors for time t; y istIs an observed value; e is the sum of squares of errors;
step S53, respectively setting the weights of the Holt-Winters model and the LSTM model as omega1、ω2Predicted value is Y1、Y2The final predicted value of the two models is YcThen the formula can be written as:
Yc＝ω1Y1+ω2Y2
obtaining the variance and covariance of the errors of the two models through the prediction errors of the two models, and setting the variance and covariance as sigma11，σ12，σ22And finally calculating the weight coefficients of the two prediction models as follows:
Figure FDA0002809991950000041
Figure FDA0002809991950000042
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CN111508598A (en) * 2020-05-06 2020-08-07 万达信息股份有限公司 Method for predicting outpatient quantity of respiratory system diseases
CN111508598B (en) * 2020-05-06 2023-12-08 万达信息股份有限公司 Respiratory disease outpatient service quantity prediction method
CN113283155A (en) * 2021-03-22 2021-08-20 中国农业科学院农业资源与农业区划研究所 Near-surface air temperature estimation method, system, storage medium and equipment
CN113283155B (en) * 2021-03-22 2024-04-09 中国农业科学院农业资源与农业区划研究所 Near-surface air temperature estimation method, system, storage medium and equipment
CN113131476A (en) * 2021-04-28 2021-07-16 南方电网科学研究院有限责任公司 Power load prediction method
CN114064203A (en) * 2021-10-28 2022-02-18 西安理工大学 Cloud virtual machine load prediction method based on multi-scale analysis and deep network model
CN114113773B (en) * 2021-11-11 2024-03-19 国网辽宁省电力有限公司经济技术研究院 Non-invasive load monitoring method based on zero sample learning
CN114113773A (en) * 2021-11-11 2022-03-01 国网辽宁省电力有限公司经济技术研究院 Non-invasive load monitoring method based on zero sample learning
CN115081702A (en) * 2022-06-14 2022-09-20 国网信息通信产业集团有限公司 Power load prediction method with interpretable characteristic, system and terminal
WO2023241439A1 (en) * 2022-06-14 2023-12-21 国网信息通信产业集团有限公司 Power load forecasting method and system having interpretable characteristics, and terminal
CN116341189A (en) * 2023-01-12 2023-06-27 山西大学 Prophet and Holt-windows combined model thermal comfort prediction method based on wavelet transformation
CN117390895B (en) * 2023-12-08 2024-02-09 华科五洲（天津）海洋工程有限公司 Semi-submersible ship ballast system simulation method, device and storage medium
CN117390895A (en) * 2023-12-08 2024-01-12 华科五洲（天津）海洋工程有限公司 Semi-submersible ship ballast system simulation method, device and storage medium
CN117977579A (en) * 2024-03-28 2024-05-03 西华大学 DWT-LSTM neural network-based adjustable load prediction method
CN117996816A (en) * 2024-03-29 2024-05-07 江苏谷峰电力科技股份有限公司 Intelligent control method and system for wind, light and firewood storage and team-level energy storage

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Application publication date: 20210312