CN111860977A - Probability prediction method and probability prediction device for short-term load - Google Patents
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Abstract
The invention discloses a probability prediction method and a probability prediction device of short-term load, wherein the method comprises the following steps: dividing historical load data recorded by all user intelligent electric meters in an area to be predicted into a training set, an integration set and a set to be predicted; carrying out related calculation aiming at historical load data in a training set to obtain a division result of a user under a plurality of different clustering quantities; performing related training on each user group after clustering according to the division result to respectively obtain a prediction model of a plurality of probability prediction results of the overall load of the region to be predicted; obtaining an integrated load probability prediction model of the area to be predicted according to the integrated concentrated historical load data, each prediction model and the real overall load value; and carrying out probability prediction on the load to be predicted in the concentrated load according to the load probability prediction model of the area to be predicted. The probability prediction method can finely utilize the historical load data of the user intelligent electric meter and improve the accuracy of the load probability prediction of the area to be predicted.
Description
Technical Field
The invention relates to the technical field of power load prediction, in particular to a probability prediction method and a probability prediction device for short-term load.
Background
In recent years, the popularity of smart meters on the user side has increased, and power grid companies can obtain high-frequency and high-resolution user load data from smart meter data when performing regional load prediction. Generally, the load of an area is usually aggregated by hundreds of user loads, and the user loads recorded by the smart meter will help to improve the accuracy of the load prediction of the whole area. Load prediction in the traditional sense is mainly to train a single model by recording the change of the overall load of the area and then constructing corresponding input features. Considering that the intelligent electric meter data is used for refining and predicting the whole load of the area, a bottom-up hierarchical load prediction method is generated, namely prediction models are respectively built for the load of a single user, and then the prediction models of all users in the area are summed up to obtain the whole prediction model.
At present, the technology for load prediction based on historical load data is mature and is put into practical use in most power grid companies at home and abroad. Scholars at Polish-Stoholwa industry university propose a model for forecasting Electric Power based on model regression (Dudek G. Pattern-based local linear regression models for short-term load forecasting [ J ]. Electric Power Systems Research,2016,130: 139-) and apply to the actual load data concentration of Polish Electric network. Researchers at the university of new south Wales, Australia, have proposed a short-term resident user load prediction model based on long-term short-term memory artificial neural network (LSTM) (Kong W, Dong Z Y, Jia Y, et al. short-term residual load for estimation based on LSTM recovery neural network [ J ]. IEEE Transactions on Smart Grid,2019,10(1):841 851.) and tested the prediction accuracy on a Smart meter dataset provided by the Intelligent Grid City, SGSC, published by the Australia government. Scholars at the university of Stephen Cleveland, UK, have adopted a bottom-up hierarchical prediction method (Stephen B, Tang X, Harvey P R, et al, incorporated reactivity the sub-profile model for short period of shaped aggregated restrained load for evaluation [ J ]. IEEE transaction Smart Grid,2017,8(4): 1591-. In general, even though a layered prediction method is adopted to improve the prediction accuracy in some researches, most methods are based on the result of a single model, and the prediction accuracy still has a space for improvement. The ensemble learning method can be used to compensate for the shortcomings of a single prediction model. Researchers at southern positive university of singapore use ensemble learning to integrate multiple wavelet transform-based prediction methods (Li S, Goel L, wang p. an intense approach for short-term load for estimating by iterative learning, 2016,170:22-29.) to improve prediction accuracy.
In the energy internet era, due to the access of distributed power supplies, electric vehicles and the like, the fluctuation of loads on a user side is more severe than before, the traditional load prediction only focuses on the load size at a certain future time, and in practical application, a power grid company always focuses more on the probability distribution condition of the future load so as to more accurately realize the depiction of random factors, namely realize the probability prediction. In recent years, methods of hierarchical prediction and integrated prediction tend to focus on point prediction, and neglect the importance of probabilistic prediction.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above.
Therefore, one purpose of the invention is to provide a short-term load probability prediction method, which can finely utilize historical load data of a user intelligent electric meter, improve the accuracy of load probability prediction of a region to be predicted, and facilitate the advanced planning of power generation operation scheduling of a power grid.
A second object of the present invention is to provide a device for predicting the probability of a short-term load.
A third object of the invention is to propose an electronic device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a method for predicting a probability of a short-term load, including: step S1, acquiring historical load data recorded by all user intelligent electric meters in the area to be predicted, and dividing the historical load data into a training set, an integration set and a set to be predicted according to a preset proportion; step S2, calculating the weekly average load curve of each user according to the historical load data in the training set, calculating the similarity matrix of the weekly average load curve among different users based on cosine similarity, and calculating the division results of the users under a plurality of different clustering quantities according to the spectral clustering method based on k-means clustering and the similarity matrix, wherein any point on the weekly average load curve is the load average value of the same user in the training set at the same time of each week; step S3, respectively training a probability prediction model of a quantile regression neural network for each user group after clustering according to the division result, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain prediction models of a plurality of probability prediction results of the overall load of the region to be predicted; step S4, calculating pinball loss functions of a minimized integration set according to historical load data of the integration set, constructing a linear programming problem and solving optimal integration weights according to each prediction model and a real overall load value, taking the pinball loss functions of the minimized integration set as a target function and the integration weights of each prediction model as optimization variables, and accumulating each prediction model by the optimal integration weights to obtain an integrated load probability prediction model of the area to be predicted; and step S5, carrying out probability prediction on the load in the to-be-predicted set according to the to-be-predicted regional load probability prediction model.
According to the short-term load probability prediction method provided by the embodiment of the invention, historical load data recorded by all user intelligent electric meters in the area to be predicted is obtained, and the historical load data is divided into a training set, an integration set and a set to be predicted according to a preset proportion. Then, aiming at historical load data in a training set, a weekly average load curve of each user is calculated, a similarity matrix of the weekly average load curve among different users is calculated based on cosine similarity, the division results of the users under a plurality of different clustering quantities are calculated according to a spectral clustering method based on k-means clustering and the similarity matrix, probability prediction models of a quantile regression neural network are respectively trained for each user group after clustering according to the division results, and output probability distribution results of the probability prediction models are accumulated through convolution to respectively obtain prediction models of a plurality of probability prediction results of the overall load of the area to be predicted. And then according to historical load data of the integrated set, calculating a pinball loss function of the minimized integrated set, according to each prediction model and a real overall load value, taking the pinball loss function of the minimized integrated set as a target function, taking the integrated weight of each prediction model as an optimization variable, constructing a linear programming problem, solving an optimal integrated weight, and accumulating each prediction model by the optimal integrated weight to obtain an integrated to-be-predicted regional load probability prediction model. And finally, carrying out probability prediction on the concentrated loads to be predicted according to the load probability prediction model of the area to be predicted. Therefore, historical load data of the user intelligent electric meter can be utilized in a refined mode, the accuracy of load probability prediction of the area to be predicted is improved, and the power generation operation scheduling of the power grid can be planned in advance.
In addition, the method for predicting the probability of the short-term load according to the embodiment of the present invention may further have the following additional technical features:
in the inventionIn one embodiment, M is the number of users in the area to be predicted, T is the number of time periods for acquiring the historical load, and T is0Recording the frequency of the load for the smart meter every day, wherein M, T and T0Are positive integers, and the step S1 specifically includes: step S11, converting the historical load data into a historical load matrix L according to a preset conversion algorithmM×TAnd is provided with LtFor the total load of the area to be predicted at time t, and set Lm,tThe load of the mth user at the time T is shown, wherein T is a positive integer less than or equal to T, and M is a positive integer less than or equal to M; step S12, the historical load matrix LM×TAccording to the sequence of time, dividing the training set into training sets LtrIntegration set LenAnd a to-be-predicted set, and a training set LtrHas a time length of TtrIntegration set LenHas a time length of Ten。
In an embodiment of the present invention, the step S2 specifically includes: step S21, training set LtrThe load in the system is averaged according to the week to obtain the week characteristic load of each userWherein T' is less than or equal to 7T0And form a cycle characteristic load matrix Vector of m-th row of the week characteristic load matrixThe weekly average power load of the mth user; step S22, according to the cycle characteristic load matrix, calculating a similarity matrix S between different users based on cosine similarityM×MThe similarity matrix SM×MRow m and column n of element Sm,nFor the electricity utilization similarity between the mth user and the nth user:
step S23, according to the spectral clustering method based on k-means clustering and the similarity matrix SM×MDividing M users into k groups to obtain a clustered user group division result;
in step S24, k is N ═ log2M]+1 value [ ·]For a rounding down function:
kj=min{2j-1,M}
wherein N is a positive integer, and j is a positive integer less than or equal to N;
In an embodiment of the present invention, the step S3 specifically includes: step S31, for k clustersjDivision result M of timejWill be subordinate to 1 st to kjThe user loads of the user groups are respectively added to obtain kjHistorical electricity load vectors of the group user group; step S32, aiming at the kjHistorical electricity load vectors of the group user groups are defined, wherein the total electricity load of the a-th group user group is a=1,2,…,kjThe total load of electricity consumption of each user group is respectively in the time period of tFor output, input characteristics are formed by the unique heat code of the week, the hour number, the load of the same time period of yesterday, the load of the last time period of yesterday, the load of the same time period of the previous day, the load of the same time period of three days before and the load of the same time period of four and three days before corresponding to the t time periodUsing training set LtrCorresponding TtrFeatures in individual time periods and corresponding outputst=1,2,…,TtrTraining kjThe quantile regression neural network is respectively recorded asWherein the input vector of the neural network corresponding to the a-th groupLength of andare the same in length; step S33, integrating the collection LenCorresponding TenIn a time period, for time T (T ═ T)tr+1,Ttr+2,…,Ttr+Ten) To input featuresRegression neural networks as quantiles respectivelyIs input to obtain kjLoad probability distribution of individual user groups; step S34, calculating k according to preset discrete convolutionjAccumulating the probability distribution of the loads to obtain a prediction model of a plurality of probability prediction results of the whole load of the area to be predicted, and setting fj,q(Xj,t) As the number of clusters is kjThe resulting prediction of the q quantile of the total load, fj,qFor the prediction model after convolution accumulation of each population, Xj,tFor the input features of the model at time t, q may be (0, 1) ]And satisfies the following formula:
Pr(Lt<fj,q(Xj,t))=q。
in an embodiment of the present invention, step S4 specifically includes: step S41, for kjIntegrating the probability distribution results of N total loads obtained under N values, and setting the integration coefficient as wjPinball loss function at q quantilesThe following were used:
whereinAnd (3) providing a q quantile prediction result for the probability prediction model, wherein y is a real overall load value, and an optimization problem is constructed through the following formula:
wherein the content of the first and second substances,is a set of quantiles for the probability prediction,as an integration set LenThe set of time periods of (a), wherein,the problem objective function is a convex function, constraint conditions are linear, the optimal integration weight is obtained after solving based on a convex optimization problem solving technology
Step S42, according to the optimal integration weight obtained in the step S41, all the prediction models under different clustering quantitiesAccumulating to obtain an integrated load probability prediction model of the area to be predicted:
wherein f isen,qRepresenting integrated probabilistic predictive models, Xt=[X1,t,X2,t,…,XN,t]And representing the input characteristics of the integrated model at the time t.
In order to achieve the above object, a second embodiment of the present invention provides a short-term load probability prediction apparatus, including: the system comprises a dividing module, a prediction module and a prediction module, wherein the dividing module is used for acquiring historical load data recorded by all user intelligent electric meters in an area to be predicted and dividing the historical load data into a training set, an integration set and a set to be predicted according to a preset proportion; the first calculation module is used for calculating a weekly average load curve of each user according to historical load data in the training set, calculating a similarity matrix of the weekly average load curves among different users based on cosine similarity, and calculating division results of the users under different clustering quantities according to a spectral clustering method based on k-means clustering and the similarity matrix, wherein any point on the weekly average load curve is the load average value of the same user in the training set at the same moment in each week; the second calculation module is used for respectively training a probability prediction model of a quantile regression neural network for each user group after clustering according to the division result, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain a prediction model of a plurality of probability prediction results of the overall load of the area to be predicted; the third calculation module is used for calculating a pinball loss function of the minimized integration set according to historical load data of the integration set, constructing a linear programming problem and solving an optimal integration weight by taking the pinball loss function of the minimized integration set as a target function and the integration weight of each prediction model as an optimization variable according to each prediction model and a real overall load value, and accumulating each prediction model by the optimal integration weight to obtain an integrated regional load probability prediction model to be predicted; and the prediction module is used for carrying out probability prediction on the load in the to-be-predicted set according to the to-be-predicted regional load probability prediction model.
According to the short-term load probability prediction device, historical load data recorded by all user intelligent electric meters in an area to be predicted are obtained through the dividing module, and the historical load data are divided into a training set, an integration set and a set to be predicted according to a preset proportion. The method comprises the steps of calculating a week average load curve of each user according to historical load data in a training set through a first calculation module, calculating a similarity matrix of the week average load curves among different users based on cosine similarity, and calculating division results of the users under different clustering quantities according to a spectral clustering method based on k-means clustering and the similarity matrix. And respectively training a probability prediction model of the quantile regression neural network for each user group after clustering according to the division result through a second calculation module, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain a prediction model of a plurality of probability prediction results of the overall load of the area to be predicted. And calculating a pinball loss function of the minimized integration set according to historical load data of the integration set through a third calculation module, constructing a linear programming problem and solving an optimal integration weight by taking the pinball loss function of the minimized integration set as a target function and the integration weight of each prediction model as an optimization variable according to each prediction model and a real overall load value, and accumulating each prediction model by the optimal integration weight to obtain an integrated load probability prediction model of the area to be predicted. And performing probability prediction on the load in the to-be-predicted concentration through a prediction module according to the load probability prediction model of the to-be-predicted region. Therefore, historical load data of the user intelligent electric meter can be utilized in a refined mode, the accuracy of load probability prediction of the area to be predicted is improved, and the power generation operation scheduling of the power grid can be planned in advance.
In order to achieve the above object, a third embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method for predicting the probability of short-term load according to the first embodiment of the present invention.
According to the electronic equipment provided by the embodiment of the invention, the processor executes the computer program stored on the memory, so that the historical load data of the user intelligent electric meter can be finely utilized, the accuracy of load probability prediction of the area to be predicted is improved, and the advance plan of power generation operation scheduling of a power grid is facilitated.
To achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, having a computer program stored thereon, where the computer program is executed by a processor to implement a method for predicting a probability of a short-term load according to an embodiment of the first aspect of the present invention.
According to the non-transitory computer-readable storage medium provided by the embodiment of the invention, through executing the stored computer program, the historical load data of the user intelligent electric meter can be finely utilized, the accuracy of load probability prediction of the area to be predicted is improved, and the advance plan of power generation operation scheduling of a power grid is facilitated.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow diagram of a method of probabilistic prediction of short term load according to one embodiment of the present invention; and
fig. 2 is a block diagram of a short-term load probability prediction device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A method for predicting the probability of a short-term load and a device for predicting the probability of a short-term load, an electronic apparatus, and a non-transitory computer-readable storage medium according to embodiments of the present invention are described below with reference to the accompanying drawings.
FIG. 1 is a flow diagram of a method of probabilistic prediction of short term load according to one embodiment of the present invention. In the embodiment of the invention, the probability prediction method of the short-term load is suitable for practical application of power grid companies, power distribution companies and power trading centers.
As shown in fig. 1, the method for predicting the probability of short-term load according to the embodiment of the present invention may include the following steps:
and S1, acquiring historical load data recorded by all user intelligent electric meters in the area to be predicted, and dividing the historical load data into a training set, an integration set and a set to be predicted according to a preset proportion. The historical load data can include metering information of the intelligent electric meter, and the preset proportion can be calibrated according to actual conditions.
To better describe the present invention, M can be set as the number of users in the area to be predicted, T is the number of time periods for acquiring the historical load, T0Recording the frequency of the load for the smart meter every day, wherein M, T and T0Are all positive integers.
Specifically, the historical load data is converted into a historical load matrix L according to a preset conversion algorithmM×TAnd is provided with LtFor the total load of the area to be predicted at time t, and set Lm,tThe load of the mth user at the time T is shown, wherein T is a positive integer less than or equal to T, and M is a positive integer less than or equal to M; step S12, history load matrix L M×TAccording to the sequence of time, dividing the training set into training sets LtrIntegration set LenAnd a to-be-predicted set, and a training set LtrHas a time length of TtrIntegration set LenHas a time length of Ten. Wherein the preset conversion algorithm can be based on actual conditionsAnd (5) calibrating the conditions. It should be noted that the training set L described in this embodimenttrAnd an integration set LenA certain ratio, such as 4 to 1, may be maintained over the length of time.
S2, aiming at historical load data in a training set, calculating a weekly average load curve of each user, calculating a similarity matrix of the weekly average load curves among different users based on cosine similarity, and calculating division results of the users under a plurality of different clustering quantities according to a spectral clustering method based on k-means clustering and the similarity matrix, wherein any point on the weekly average load curve is the load average value of the same user in the training set at the same time in each week.
It should be noted that the spectral clustering method of k-means clustering described in this embodiment is a clustering method based on a similarity matrix, and it is considered that M subjects are divided into k populations according to the degree of similarity, so that the intra-population similarity is high, and the similarity between the populations is low, where M and k are both positive integers. The division is realized by the following steps:
First, a similarity matrix S of the subject is obtained, which is positive and symmetric, and all elements are non-negative. According to the theory of linear algebra, S can be subjected to the following spectral decomposition (eigenvalue decomposition):
S=QΛQT
wherein Λ is a diagonal matrix, and diagonal elements are characteristic values of S, which are arranged in a sequence from small to large; q is an orthogonal matrix, and the column vector of the orthogonal matrix is the eigenvector corresponding to the eigenvalue.
Taking the first k columns of Q to form a spectral feature matrix V of the subjectM×kTo V pairM×kAnd (5) clustering by using the k-means to obtain a user group division result after clustering. The theory and application of spectral clustering based on k-means clustering is described in detail in (Ng A Y, Jordan MI, Weiss Y. on spectral clustering: Analysis and an algorithm [ C)]//Advances inNeural Information Processing Systems.2002:849-856)。
In an embodiment of the present invention, step S2 may specifically include:
step S21, training set LtrMedium load, according to weekly fetchAverage value to obtain week characteristic load of each userWherein T' is less than or equal to 7T0And form a cycle characteristic load matrixVector of m-th row of cycle characteristic load matrixThe weekly average power load of the mth user.
Step S22, according to the week characteristic load matrix, calculating the similarity matrix S between different users based on cosine similarity M×MSimilarity matrix SM×MRow m and column n of element Sm,nThe electricity utilization similarity between the mth user and the nth user.
Step S23, according to the spectral clustering method based on k-means clustering and the similarity matrix SM×MAnd dividing the M users into k groups to obtain the clustered user group division result.
In step S24, k is N ═ log2M]+1 value [ ·]For a rounding down function:
kj=min{2j-1,M}
wherein N is a positive integer, and j is a positive integer less than or equal to N.
Step S25, repeating the above step S23 to obtain N different groups of division resultsAnd obtaining the division result of the user under a plurality of different clustering quantities.
And S3, respectively training a probability prediction model of the quantile regression neural network for each user group after clustering according to the division result, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain a prediction model of a plurality of probability prediction results of the overall load of the region to be predicted.
It should be noted that the quantile regression neural network described in this embodiment is a supervised neural network model, and can provide a probability distribution of output under a certain input characteristic. The input feature vector is set as X, the output random variable is set as y, and the method is based on the traditional fully-connected neural network and is used for estimating the condition distribution of the random variable y | X by training parameters such as weight, bias and the like of neurons in the network through training methods such as gradient descent and the like. The trained quantile regression neural network model may use the symbol f q(X) represents, wherein q ∈ (0, 1)]Indicating the size of the quantile. The meaning of quantile is that event y ≦ fqThe probability of (X) | X occurring is exactly q, i.e.:
Pr(y≤fq(X)|X)=q
the theory of quantile regression neural network is described in detail (Taylor J W.A qualitative regression neural network approach to influencing the conditional severity of multiprodrenching [ J ]. Journal of Forecasting,2000,19(4): 299-311).
In an embodiment of the present invention, step S3 may specifically include:
step S31, for k clustersjDivision result of timeWill be subordinate to 1 st to kjThe user loads of the user groups are respectively added to obtain kjHistorical electricity load vectors for the group of users.
Step S32, for kjHistorical electricity load vectors of the group user groups are defined, wherein the total electricity load of the a-th group user group isa=1,2,…,kjThe total load of electricity consumption of each user group is respectively in the time period of tFor output, input characteristics are formed by the unique heat code of the week, the hour number, the load of the same time period of yesterday, the load of the last time period of yesterday, the load of the same time period of the previous day, the load of the same time period of three days before and the load of the same time period of four and three days before corresponding to the t time periodUsing training set LtrCorresponding TtrFeatures in individual time periods and corresponding outputs t=1,2,…,TtrTraining kjThe quantile regression neural network is respectively recorded asWherein the input vector of the neural network corresponding to the a-th groupLength of andare the same length.
It should be noted that the week-only encoding described in this embodiment applies a single-hot encoding technique, which is an encoding technique for discrete features, also called bit-efficient encoding, by using N-bit boolean variables to encode N states. For example, assuming that there are 3 values for a discrete feature, i.e., 1, 2, and 3, the one-hot codes for those three states are 001, 010, and 100, respectively, and occupy 3-bit boolean variables.
Step S33, integrating the collection LenCorresponding TenIn a time period, for time T (T ═ T)tr+1,Ttr+2,…,Ttr+Ten) To input featuresRegression neural networks as quantiles respectivelyIs input to obtain kjLoad probability distribution of individual user groups; .
Step S34, calculating k according to preset discrete convolutionjAccumulating the probability distribution of the individual loads to obtain a prediction model of a plurality of probability prediction results of the overall load of the area to be predicted, and setting 1fj,q(Xj,t) As the number of clusters is kjThe resulting prediction of the q quantile of the total load, fj,qFor the prediction model after convolution accumulation of each population, X j,tFor the input features of the model at time t, q may be (0, 1)]And satisfies the following formula:
Pr(Lt<fj,q(Xj,t))=q。
s4, calculating pinball loss functions of the minimized integration set according to historical load data of the integration set, constructing a linear programming problem and solving optimal integration weights according to each prediction model and a real overall load value by taking the pinball loss functions of the minimized integration set as a target function and the integration weights of each prediction model as optimization variables, and accumulating each prediction model by the optimal integration weights to obtain an integrated regional load probability prediction model to be predicted.
Specifically, for kjIntegrating the probability distribution results of N total loads obtained under N values, and setting the integration coefficient as wjPinball loss function at q quantilesThe following were used:
whereinQ quantile prediction results are given for the probability prediction model, and y is the real overall load value, wherein the following formula is used for predicting the load valueConstructing an optimization problem:
wherein the content of the first and second substances,is a set of quantiles for the probability prediction,as an integration set LenThe set of time periods of (a), wherein,the problem objective function is a convex function, constraint conditions are linear, the optimal integration weight is obtained after solving based on a convex optimization problem solving technology
The prediction models under different clustering numbers are subjected to the optimal integration weight obtained in the step S41Accumulating to obtain an integrated load probability prediction model of the area to be predicted:
wherein f isen,qRepresenting integrated probabilistic predictive models, Xt=[X1,t,X2,t,…,XN,t]And representing the input characteristics of the integrated model at the time t.
And S5, carrying out probability prediction on the load concentrated to be predicted according to the load probability prediction model of the area to be predicted.
In the embodiment of the invention, the short-term load probability prediction method realizes the utilization of the fine-granularity user load recorded by the intelligent electric meter, establishes the short-term coincidence probability prediction method based on user clustering and ensemble learning, fully utilizes the information provided by the intelligent electric meter, considers the cluster characteristics of the user load, divides the users by using clustering, realizes the probability load prediction from bottom to top and can predict the probability distribution of the regional load with high accuracy compared with the prior art. By the method, the load condition at the future moment can be better estimated, so that the safety and economy of system operation scheduling are improved, and the method is beneficial to improving the operation organization benefits of power grid companies, power distribution companies and power markets, so that the method has important practical significance and good application prospect.
In summary, according to the short-term load probability prediction method provided by the embodiment of the invention, historical load data recorded by all user intelligent electric meters in the area to be predicted is obtained, and the historical load data is divided into a training set, an integration set and a set to be predicted according to a preset proportion. Then, aiming at historical load data in a training set, a weekly average load curve of each user is calculated, a similarity matrix of the weekly average load curve among different users is calculated based on cosine similarity, the division results of the users under a plurality of different clustering quantities are calculated according to a spectral clustering method based on k-means clustering and the similarity matrix, probability prediction models of a quantile regression neural network are respectively trained for each user group after clustering according to the division results, and output probability distribution results of the probability prediction models are accumulated through convolution to respectively obtain prediction models of a plurality of probability prediction results of the overall load of the area to be predicted. And then according to historical load data of the integrated set, calculating a pinball loss function of the minimized integrated set, according to each prediction model and a real overall load value, taking the pinball loss function of the minimized integrated set as a target function, taking the integrated weight of each prediction model as an optimization variable, constructing a linear programming problem, solving an optimal integrated weight, and accumulating each prediction model by the optimal integrated weight to obtain an integrated to-be-predicted regional load probability prediction model. And finally, carrying out probability prediction on the concentrated loads to be predicted according to the load probability prediction model of the area to be predicted. Therefore, historical load data of the user intelligent electric meter can be utilized in a refined mode, the accuracy of load probability prediction of the area to be predicted is improved, and the power generation operation scheduling of the power grid can be planned in advance.
Fig. 2 is a block diagram of a short-term load probability prediction device according to an embodiment of the present invention.
As shown in fig. 2, a short-term load probability prediction apparatus 1000 according to an embodiment of the present invention includes: a partitioning module 100, a first calculation module 200, a second calculation module 300, a third calculation module 400 and a prediction module 500.
The dividing module 100 is configured to obtain historical load data recorded by all user smart electric meters in an area to be predicted, and divide the historical load data into a training set, an integration set, and a set to be predicted according to a preset proportion.
The first calculating module 200 is configured to calculate a weekly average load curve of each user according to historical load data in a training set, calculate a similarity matrix of the weekly average load curve between different users based on cosine similarity, and calculate a partitioning result of the users under a plurality of different clustering numbers according to a spectral clustering method based on k-means clustering and the similarity matrix, where any point on the weekly average load curve is a load average value of the same user in the training set at the same time in each week.
The second calculation module 300 is configured to train a probability prediction model of the quantile regression neural network for each user group after clustering according to the partition result, and add up the output probability distribution results of the probability prediction model through convolution, so as to obtain a prediction model of multiple probability prediction results of the overall load of the region to be predicted.
The third calculation module 400 is configured to calculate a pinball loss function of the minimized integration set according to the historical load data of the integration set, construct a linear programming problem and solve an optimal integration weight according to each prediction model and the real overall load value, with the pinball loss function of the minimized integration set as a target function and the integration weight of each prediction model as an optimization variable, and accumulate each prediction model with the optimal integration weight to obtain an integrated load probability prediction model of the area to be predicted.
The prediction module 500 is configured to perform probability prediction on the load in the to-be-predicted set according to the load probability prediction model of the to-be-predicted region.
It should be noted that, details that are not disclosed in the short-term load probability prediction apparatus according to the embodiment of the present invention are referred to details that are disclosed in the short-term load probability prediction method according to the embodiment of the present invention, and detailed description thereof is omitted here.
In summary, the device for predicting the short-term load probability of the embodiment of the invention obtains the historical load data recorded by all the user intelligent electric meters in the area to be predicted through the dividing module, and divides the historical load data into a training set, an integration set and a set to be predicted according to the preset proportion. The method comprises the steps of calculating a week average load curve of each user according to historical load data in a training set through a first calculation module, calculating a similarity matrix of the week average load curves among different users based on cosine similarity, and calculating division results of the users under different clustering quantities according to a spectral clustering method based on k-means clustering and the similarity matrix. And respectively training a probability prediction model of the quantile regression neural network for each user group after clustering according to the division result through a second calculation module, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain a prediction model of a plurality of probability prediction results of the overall load of the area to be predicted. And calculating a pinball loss function of the minimized integration set according to historical load data of the integration set through a third calculation module, constructing a linear programming problem and solving an optimal integration weight by taking the pinball loss function of the minimized integration set as a target function and the integration weight of each prediction model as an optimization variable according to each prediction model and a real overall load value, and accumulating each prediction model by the optimal integration weight to obtain an integrated load probability prediction model of the area to be predicted. And performing probability prediction on the load in the to-be-predicted concentration through a prediction module according to the load probability prediction model of the to-be-predicted region. Therefore, historical load data of the user intelligent electric meter can be utilized in a refined mode, the accuracy of load probability prediction of the area to be predicted is improved, and the power generation operation scheduling of the power grid can be planned in advance.
In order to implement the foregoing embodiments, the present invention further provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting the probability of short-term load of the foregoing embodiments.
According to the electronic equipment provided by the embodiment of the invention, the processor executes the computer program stored on the memory, so that the historical load data of the user intelligent electric meter can be finely utilized, the accuracy of load probability prediction of the area to be predicted is improved, and the advance plan of power generation operation scheduling of a power grid is facilitated.
In order to implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method for predicting the probability of short-term load of the foregoing embodiments.
According to the non-transitory computer-readable storage medium provided by the embodiment of the invention, through executing the stored computer program, the historical load data of the user intelligent electric meter can be finely utilized, the accuracy of load probability prediction of the area to be predicted is improved, and the advance plan of power generation operation scheduling of a power grid is facilitated.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. A method for probabilistic prediction of short-term load, comprising:
Step S1, acquiring historical load data recorded by all user intelligent electric meters in the area to be predicted, and dividing the historical load data into a training set, an integration set and a set to be predicted according to a preset proportion;
step S2, calculating the weekly average load curve of each user according to the historical load data in the training set, calculating the similarity matrix of the weekly average load curve among different users based on cosine similarity, and calculating the division results of the users under a plurality of different clustering quantities according to the spectral clustering method based on k-means clustering and the similarity matrix, wherein any point on the weekly average load curve is the load average value of the same user in the training set at the same time of each week;
step S3, respectively training a probability prediction model of a quantile regression neural network for each user group after clustering according to the division result, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain prediction models of a plurality of probability prediction results of the overall load of the region to be predicted;
step S4, calculating pinball loss functions of a minimized integration set according to historical load data of the integration set, constructing a linear programming problem and solving optimal integration weights according to each prediction model and a real overall load value, taking the pinball loss functions of the minimized integration set as a target function and the integration weights of each prediction model as optimization variables, and accumulating each prediction model by the optimal integration weights to obtain an integrated load probability prediction model of the area to be predicted;
And step S5, carrying out probability prediction on the load in the to-be-predicted set according to the to-be-predicted regional load probability prediction model.
2. The method of claim 1, wherein M is the number of users in the area to be predicted, T is the number of time periods to obtain historical load, and T is the number of historical load acquisition periods0Recording the frequency of the load for the smart meter every day, wherein M, T and T0Are positive integers, and the step S1 specifically includes:
step S11, converting the historical load data into a historical load matrix L according to a preset conversion algorithmM×TAnd is provided with LtFor the total load of the area to be predicted at time t, and set Lm,tThe load of the mth user at the time T is shown, wherein T is a positive integer less than or equal to T, and M is a positive integer less than or equal to M;
step S12, the historical load matrix LM×TAccording to the sequence of time, dividing the training set into training sets LtrIntegration set LenAnd a to-be-predicted set, and a training set LtrHas a time length of TtrIntegration set LenHas a time length of Ten。
3. The method for predicting the probability of the short-term load according to claim 2, wherein the step S2 specifically comprises:
step S21, training set LtrThe load in the system is averaged according to the week to obtain the week characteristic load of each user Wherein T' is less than or equal to 7T0And form a cycle characteristic load matrixVector of m-th row of the week characteristic load matrixThe weekly average power load of the mth user;
step S22, according to the cycle characteristic load matrix, calculating a similarity matrix S between different users based on cosine similarityM×MThe similarity matrix SM×MRow m and column n of element Sm,nFor the electricity utilization similarity between the mth user and the nth user:
step S23, according to the spectral clustering method based on k-means clustering and the similarity matrix SM×MDividing M users into k groups to obtain a clustered user group division result;
in step S24, k is N ═ log2M]+1 value [ ·]For a rounding down function:
kj=min{2j-1,M}
wherein N is a positive integer, and j is a positive integer less than or equal to N;
4. The method for probabilistic prediction of short term load according to claim 3, wherein said step S3 specifically comprises:
step S31, for k clustersjDivision result of timeWill be subordinate to 1 st to kjThe user loads of the user groups are respectively added to obtain kjHistorical electricity load vectors of the group user group;
Step S32, aiming at the kjHistorical electricity load vectors of the group user groups are defined, wherein the total electricity load of the a-th group user group isThe total load of electricity consumption of each user group in t time periodFor output, input characteristics are formed by the unique heat code of the week, the hour number, the load of the same time period of yesterday, the load of the last time period of yesterday, the load of the same time period of the previous day, the load of the same time period of three days before and the load of the same time period of four and three days before corresponding to the t time periodUsing training set LtrCorresponding TtrFeatures in individual time periods and corresponding outputsTraining kjThe quantile regression neural network is respectively recorded asWherein the input vector of the neural network corresponding to the a-th groupLength of andare the same in length;
step S33, integrating the collection LenCorresponding TenIn a time period, for time T (T ═ T)tr+1,Ttr+2,…,Ttr+Ten) To input featuresRegression neural networks as quantiles respectivelyIs input to obtain kjLoad probability distribution of individual user groups;
step S34, calculating k according to preset discrete convolutionjAccumulating the probability distribution of the loads to obtain a prediction model of a plurality of probability prediction results of the whole load of the area to be predicted, and setting fj,q(Xj,t) As the number of clusters is kjThe resulting prediction of the q quantile of the total load, f j,qFor the prediction model after convolution accumulation for each population,for the input features of the model at time t, q may be (0, 1)]And satisfies the following formula:
Pr(Lt<fj,q(Xj,t))=q。
5. the method for predicting the probability of the short-term load according to claim 4, wherein the step S4 specifically comprises:
step S41, for kjIntegrating the probability distribution results of N total loads obtained under N values, and setting the integration coefficient as wjPinball loss function at q quantilesThe following were used:
whereinAnd (3) providing a q quantile prediction result for the probability prediction model, wherein y is a real overall load value, and an optimization problem is constructed through the following formula:
wherein the content of the first and second substances,is a set of quantiles for the probability prediction,as an integration set LenThe set of time periods of (a), wherein,the problem objective function is a convex function, constraint conditions are linear, the optimal integration weight is obtained after solving based on a convex optimization problem solving technology
Step S42, according to the optimal integration weight obtained in the step S41, all the prediction models under different clustering quantitiesAre added up to obtainThe integrated load probability prediction model of the area to be predicted comprises the following steps:
wherein f isen,qRepresenting integrated probabilistic predictive models, X t=[X1,t,X2,t,…,XN,t]And representing the input characteristics of the integrated model at the time t.
6. A device for predicting the probability of a short-term load, comprising:
the system comprises a dividing module, a prediction module and a prediction module, wherein the dividing module is used for acquiring historical load data recorded by all user intelligent electric meters in an area to be predicted and dividing the historical load data into a training set, an integration set and a set to be predicted according to a preset proportion;
the first calculation module is used for calculating a weekly average load curve of each user according to historical load data in the training set, calculating a similarity matrix of the weekly average load curves among different users based on cosine similarity, and calculating division results of the users under different clustering quantities according to a spectral clustering method based on k-means clustering and the similarity matrix, wherein any point on the weekly average load curve is the load average value of the same user in the training set at the same moment in each week;
the second calculation module is used for respectively training a probability prediction model of a quantile regression neural network for each user group after clustering according to the division result, and performing convolution accumulation on output probability distribution results of the probability prediction model to respectively obtain a prediction model of a plurality of probability prediction results of the overall load of the area to be predicted;
The third calculation module is used for calculating a pinball loss function of the minimized integration set according to historical load data of the integration set, constructing a linear programming problem and solving an optimal integration weight by taking the pinball loss function of the minimized integration set as a target function and the integration weight of each prediction model as an optimization variable according to each prediction model and a real overall load value, and accumulating each prediction model by the optimal integration weight to obtain an integrated regional load probability prediction model to be predicted;
and the prediction module is used for carrying out probability prediction on the load in the to-be-predicted set according to the to-be-predicted regional load probability prediction model.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of probabilistic prediction of short term load as claimed in any of claims 1 to 5.
8. A non-transitory computer readable storage medium having stored thereon a computer program, the program being executable by a processor to implement a method of probabilistic prediction of short term load as claimed in any one of claims 1 to 5.
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CN116805785A (en) * | 2023-08-17 | 2023-09-26 | 国网浙江省电力有限公司金华供电公司 | Power load hierarchy time sequence prediction method based on random clustering |
CN116805785B (en) * | 2023-08-17 | 2023-11-28 | 国网浙江省电力有限公司金华供电公司 | Power load hierarchy time sequence prediction method based on random clustering |
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