CN112836885A - Combined load prediction method, combined load prediction device, electronic equipment and storage medium - Google Patents

Combined load prediction method, combined load prediction device, electronic equipment and storage medium Download PDF

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CN112836885A
CN112836885A CN202110179158.XA CN202110179158A CN112836885A CN 112836885 A CN112836885 A CN 112836885A CN 202110179158 A CN202110179158 A CN 202110179158A CN 112836885 A CN112836885 A CN 112836885A
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马振祺
马志程
张光儒
杨军亭
苏娟
邢广进
方舒
张家午
温定筠
吴建军
拜润卿
沈渭程
梁有珍
张秀斌
高磊
朱亮
张艳丽
蒋臣
智勇
张睿
张凯
李亚昕
田阔
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
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China Agricultural University
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
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Abstract

The embodiment of the invention provides a combined load prediction method, a combined load prediction device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining the value of a forecast meteorological factor, a basic load, a response load, a load before and after response and an electricity price of a day to be predicted; inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model, and outputting a power grid load forecasting value; the forecasting model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days; the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load on the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data. The invention realizes accurate prediction of the load participating in demand response.

Description

Combined load prediction method, combined load prediction device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of power system load prediction technologies, and in particular, to a combined load prediction method and apparatus, an electronic device, and a storage medium.
Background
With the continuous increase of social economy, the power demand of people increases, the power load rapidly increases, and huge impact is brought to the planning and operation of a power distribution network.
With the deep market reform and the rapid development of intelligent demand response, the generalized load is accessed to the power distribution network system in a large scale, and great influence is caused on the aspects of system operation, resource allocation and the like. Different from the traditional load, the active load is a load which can be used by a power consumer in a certain period of the future according to the change of the electricity price in the power market, the electricity economy of the power consumer and the safety and stability of a system are realized, the original electricity load curve is changed, and obviously, the traditional load prediction method cannot accurately predict the load participating in demand response.
At present, active load prediction at home and abroad mainly comprises analysis of corresponding influence factors and analysis of user electricity utilization characteristics, and new requirements of an active power distribution network on load prediction are provided. In the face of uncertainty of load operation and complexity of load influence factors, a single load prediction method is difficult to accurately predict short-term load.
Disclosure of Invention
The embodiment of the invention provides a combined load prediction method, a combined load prediction device, electronic equipment and a storage medium, which are used for solving the problem that the existing load prediction method cannot accurately predict loads participating in demand response.
In a first aspect, an embodiment of the present invention provides a combined load prediction method, including:
determining the value of a forecast meteorological factor, a basic load, a response load, a load before and after response and an electricity price of a day to be predicted;
inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model;
the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days;
the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
Preferably, the prediction model comprises a base load prediction model, a response load prediction model and a superposition model;
inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model, wherein the power grid load forecasting value comprises the following steps:
inputting the value of the forecast environment factor of the day to be forecasted and the basic load into the basic load forecasting model, and outputting a basic load forecasting value;
inputting the response load of the day to be predicted, the load before and after response and the electricity price into the response load prediction model, and outputting a predicted value of the demand response load;
and inputting the basic load predicted value and the demand response load predicted value into the superposition model, and outputting a power grid load predicted value of a day to be predicted.
Preferably, the basic load prediction model is obtained by performing short-term basic load prediction training based on the values of the actual meteorological factors and the sample data of the basic load on the plurality of historical days;
and completing short-term base load prediction training based on the actual meteorological factor value and the base load in the sample data, wherein the method comprises the following steps:
s1, inputting the actual meteorological factor value and the basic load in the sample data into a CS-SVM (support vector machine-support vector machine) model of the brook optimization support vector machine set by initial parameters including a penalty coefficient, the width of a radial basis kernel function, an initial probability, a nest position and the maximum iteration number to obtain the optimal fitness value of the nest;
s2, determining the current optimal bird nest position according to the optimal fitness value of the bird nest, updating other bird nest positions according to the current optimal bird nest position to generate a group of new bird nest positions, and comparing the current optimal bird nest position with the group of new bird nest positions to obtain a prediction error;
s3, obtaining a better bird nest position according to the prediction error, comparing the probability of eliminating cuckoo eggs with the numerical value of the random number, replacing the current optimal bird nest position with a group of new bird nest positions when the probability of eliminating cuckoo eggs is smaller than the random number, judging whether the current iteration end condition is met, jumping out to find the value of the optimal bird nest position if the current iteration end condition is met, and returning to S2 to continuously find the optimal bird nest position if the current iteration end condition is not met; wherein the iteration end condition comprises a maximum iteration number or an optimal fitness value determined by the width of a radial basis kernel function;
and S4, updating parameters in the CS-SVM model of the Cuckoo optimization support vector machine to establish a basic load prediction model for the optimal bird nest position, and completing short-term basic load prediction training.
Preferably, the response load prediction model is obtained by performing short-term response load prediction training based on the response loads of the plurality of historical days, the load before and after response and the sample data of the electricity price;
completing short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data, and comprising the following steps:
constructing an error minimum objective function according to the response load, the load before and after response and the electricity price in the sample data;
solving the target function with the minimum error based on a gray wolf optimization algorithm for setting the number of wolf clusters, the maximum number of iterations, the direction correction probability and the initialized coordinate space of the wolf clusters to obtain a demand response elastic matrix;
and establishing the response load prediction model based on the demand response elastic matrix to finish short-term response load prediction training.
Preferably, the constructing an objective function with a minimum error according to the response load, the load before and after the response and the electricity price in the sample data includes:
respectively obtaining the load demand variation delta q of the i-th day period of the M day based on the load before and after the 24-time-period response in the M days and the electricity pricei(m) and corresponding rate of change of electricity price Δ pi(m)；
A load demand variation Δ q based on the m-th day i periodi(m) and corresponding rate of change of electricity price Δ pi(m) obtaining a vector of products made between the demand amount of the responsive preload participating in the demand response at the i-th day period and the rate of change in the electricity prices at the 24 i-th day period
Figure BDA0002940941580000041
Based on the product vector
Figure BDA0002940941580000042
And demand response price elastic coefficient to obtain the m-th day i time interval participation demand response variable quantity
Figure BDA0002940941580000043
Estimated value of Δ q'i m；
Participating in demand response variation based on the i-th day
Figure BDA0002940941580000045
Estimated value of Δ q'i mAnd said product vector qi mAnd constructing an error minimum objective function.
Preferably, the solving the objective function with the minimum error by the gray wolf optimization algorithm based on the set number of wolf clusters, the maximum number of iterations, the direction correction probability and the initialized coordinate space of the wolf clusters to obtain the demand response elastic matrix includes:
updating the wolf pack position according to a preset rule to obtain the characteristic feature that a new generation of wolf individuals form a new wolf pack, and judging whether the maximum iteration frequency is reached currently;
if the maximum number of iterations is reached, judging whether the 24 time intervals are met, if so, outputting a demand response elastic matrix, otherwise, re-optimizing the demand price elastic coefficient of the next time interval, and outputting the demand response elastic matrix until 24 time intervals are executed;
if the iteration maximum number is not reached, updating the position of the wolf pack according to the preset rule again, adding 1 to the iteration number, and continuously executing the step of updating the position of the wolf pack according to the preset rule until the iteration maximum number is reached.
Preferably, the updating the wolf pack position according to the predetermined rule to obtain the new generation wolf individuals to form the new wolf pack characteristics includes: updating the position of the wolf group according to rules including surrounding, hunting and updating when the gray wolf is hunted, generating a new generation gray wolf individual, and combining the father generation and the generated new offspring generation through optimization to form a new wolf group characteristic.
In a second aspect, an embodiment of the present invention provides a combined load prediction apparatus, including:
the data determining unit is used for determining the value of the forecast meteorological factor, the basic load, the response load, the load before and after the response and the electricity price of the day to be predicted;
the load prediction unit is used for inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be predicted into a prediction model to obtain a power grid load prediction value output by the prediction model;
the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days;
the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the combined load prediction method according to any one of the above-mentioned first aspects when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the combined load prediction method according to any one of the above-mentioned first aspects.
According to the combined load forecasting method, the combined load forecasting device, the combined electronic equipment and the combined storage medium, the forecasting value of the weather factor of the day to be forecasted, the basic load, the response load, the load before and after response and the electricity price are input into the forecasting model, the power grid load forecasting value is output, accurate forecasting of the load participating in demand response is achieved, and the problem that the load in short term is difficult to accurately forecast by a single load forecasting method facing uncertainty of load operation and complexity of load influence factors is solved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a combined load prediction method provided by the present invention;
FIG. 2 is a block diagram of a predictive model provided by the present invention;
FIG. 3 is a schematic diagram of basic load prediction model training provided by the present invention;
FIG. 4 is a schematic diagram of the response load prediction model training provided by the present invention;
FIG. 5 is a comparison of the effect of short term base load prediction provided by the present invention;
FIG. 6 is a graph comparing a predicted response load curve to an actual load curve provided by the present invention;
FIG. 7 is a graph comparing predicted load values to actual load values after response provided by the present invention;
FIG. 8 is a schematic structural diagram of a combined load prediction device provided in the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A combined load prediction method, apparatus, electronic device and storage medium according to the present invention are described below with reference to fig. 1 to 9.
The embodiment of the invention provides a combined load prediction method. Fig. 1 is a schematic flowchart of a combined load prediction method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 110, determining the value of the forecast weather factor, the basic load, the response load, the load before and after the response and the electricity price of the day to be predicted;
specifically, values of temperature and humidity forecast, load data, load before and after response and electricity price of a day to be predicted are obtained, and the load data of the day to be predicted are decomposed into a basic load and a response load based on a fractal characteristic correction similar day method; the base load is independent of a price factor and the response load is dependent on a price factor.
Step 120, inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after the response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model;
the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days;
the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
Specifically, after sampling the actual temperature and humidity values, the load data and the load data before and after response of a plurality of historical days, the load data in the sample data is decomposed into a basic load and a response load based on a fractal characteristic correction similar day method, the basic load in the sample data is irrelevant to a price factor, and the response load in the sample data is relevant to the price factor.
According to the method provided by the embodiment of the invention, the prediction model is trained based on the actual meteorological factor values, the basic load, the response load, the load before and after response and the sample data of the electricity price on a plurality of historical days, and the power grid load prediction value of the day to be predicted is obtained by inputting the forecast meteorological factor values, the basic load, the response load, the load before and after response and the electricity price of the day to be predicted into the prediction model, so that the load participating in demand response in the power grid system can be accurately predicted.
Based on any of the above embodiments, as shown in fig. 2, the prediction model includes a base load prediction model 210, a response load prediction model 220, and an overlay model 230;
inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model, wherein the power grid load forecasting value comprises the following steps:
inputting the value of the forecast environment factor and the basic load of the day to be forecasted into the basic load forecasting model 210, and outputting a basic load forecasting value;
inputting the response load of the day to be predicted, the load before and after response and the electricity price to the response load prediction model 220, and outputting a predicted value of the demand response load;
and inputting the basic load predicted value and the demand response load predicted value into the superposition model 230, and outputting a power grid load predicted value of the day to be predicted.
Specifically, the basic load predicted value and the demand response load predicted value are added to obtain a final power grid load predicted value.
Based on any of the above embodiments, the base load prediction model 210 is obtained by performing short-term base load prediction training based on the actual meteorological factor values and the sample data of the base load of the plurality of historical days;
as shown in fig. 3, the basic load prediction model training schematic diagram completes short-term basic load prediction training based on the actual meteorological factor value and the basic load in the sample data, and includes the following steps:
inputting the actual meteorological factor value and the basic load in the sample data into a CS-SVM model of the cuckoo optimization support vector machine set by initial parameters including a penalty coefficient, the width of a radial basis kernel function, an initial probability, a nest position and the maximum iteration number to obtain the optimal fitness value of the nest;
specifically, the following 3 ideal states are assumed to simulate the behavior of cuckoo searching for nests to show the cuckoo optimization search CS algorithm, specifically as follows:
1) assuming that each cuckoo produces only one bird egg at each egg laying, the hatching position of the bird eggs is placed in any nest in a random manner.
2) And randomly extracting a group of bird nest positions by adopting a random method, and taking the bird nest positions as the optimal bird nest (optimal solution) to be transmitted to the next generation.
3) Setting the number of cuckoo nests to be N, wherein the probability of finding cuckoo eggs in the process of searching nests of cuckoo nests is Pa。
Based on the assumed ideal state, the path and the position of the cuckoo nest are updated by adopting the formula (1):
Figure BDA0002940941580000091
in the formula:
Figure BDA0002940941580000092
representing the position of the offspring nest of the tth generation laying valley bird in the ith nest;
Figure BDA0002940941580000093
the step control amount is shown; x is the number oft,bestFor the optimal solution of the t-th generation cuckoo nest iteration, α0Is a constant, 0.01;
Figure BDA0002940941580000094
representing a dot product; l (λ) represents a search path, which obeys a Levy distribution:
Levy～u＝t-λ(1≤λ≤3) (2)
the symmetrical Levy stable distribution is obtained by calculation according to a Mantegna algorithm, and a specific calculation formula is shown as a formula (3):
Figure BDA0002940941580000095
in the formula: u, V are all normal distributions and λ is 1.5.
Wherein σ2The calculation formula (4) is as follows:
Figure BDA0002940941580000096
in the formula: Γ is the standard Gamma function.
According to the assumed third ideal state, the probability that the brook bird nest finds the bird egg in the nest searching process is PaThe nest master may discard or create a new nest, which is equivalent to discarding a portion of the solutions, i.e., iteratively producing new solutions as follows:
Figure BDA0002940941580000097
where γ means a scaling factor, is a standard (0,1) distribution, used to monitor the probability that a newly generated solution is replaced,
Figure BDA0002940941580000101
two random solutions during the t-th iteration.
Determining the current optimal bird nest position according to the optimal fitness value of the bird nest, updating other bird nest positions according to the current optimal bird nest position to generate a group of new bird nest positions, and comparing the current optimal bird nest position with the group of new bird nest positions to obtain a prediction error;
obtaining a better bird nest position according to the prediction error, comparing the probability of eliminating cuckoo eggs with the numerical value of the random number, replacing the current optimal bird nest position with a group of new bird nest positions when the probability of eliminating cuckoo eggs is smaller than the random number, judging whether the current iteration end condition is met, jumping out to find the value of the optimal bird nest position if the current iteration end condition is met, and otherwise, executing to continuously find the optimal bird nest position; wherein the iteration end condition comprises a maximum iteration number or an optimal fitness value determined by the width of a radial basis kernel function;
and updating parameters in the CS-SVM model of the cuckoo optimization support vector machine to establish a basic load prediction model for the optimal bird nest position, and completing short-term basic load prediction training.
The embodiment of the invention substitutes the actual meteorological factor value and the basic load in the sample data into the CS-SVM model of the Cuckoo optimization support vector machine, iteratively searches the optimal bird nest position corresponding to the optimal adaptive value, namely introduces the CS-SVM model to realize the short-term basic load prediction training.
It should be noted that the short-term base load prediction model training based on the CS-SVM includes the following steps:
(1) inputting model data and preprocessing operation;
(2) setting parameters of the CS optimization SVM: penalty coefficient C, width sigma of radial basis function, and initial probability PaThe position (N) of the bird nest, the maximum number of iterations, etc.;
(3) determining the current optimal solution (optimal bird nest position) according to the bird nest optimal fitness value;
(4) updating the positions of other cuckoo bird nests through the optimal solution to generate a group of new cuckoo bird nest positions, and calculating the position of a better bird nest;
(5) comparing the nest position of the previous generation cuckoo with the nest position of the new generation cuckoo according to the predicted error value to obtain a better nest position;
(6) to Pa(probability of eliminating cuckoo egg) is compared with the value of r (random number), when P isaIf the value is less than r, replacing the original (abandoned) position with the newly generated cuckoo nest position, comparing the fitness function values before and after the adjustment, and finally selecting the optimal cuckoo nest position and the fitness value;
(7) judging whether an iteration ending condition (maximum iteration times or an optimal fitness function) is reached, if the iteration ending condition is met, jumping out to seek optimization, otherwise, returning to continue to seek optimization;
(8) and (C, sigma) values of the optimal bird nest positions are used as parameter setting values of the support vector machine, and a short-term basic load prediction model based on the CS-SVM is established.
Based on any of the above embodiments, the response load prediction model 220 is obtained by performing short-term response load prediction training based on the sample data of the response loads, the loads before and after response and the electricity prices of the plurality of historical days;
as shown in fig. 4, the response load prediction model training diagram completes short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data, and includes the following steps:
410, constructing an objective function with the minimum error according to the response load, the load before and after response and the electricity price in the sample data;
420, solving the objective function with the minimum error based on a gray wolf optimization algorithm for setting the number of wolf clusters, the maximum number of iterations, the direction correction probability and the initialized coordinate space of the wolf clusters to obtain a demand response elastic matrix;
430, establishing the response load prediction model based on the demand response elastic matrix, and completing short-term response load prediction training.
The embodiment of the invention substitutes the response load, the load before and after response and the electricity price in the sample data into the gray wolf optimization algorithm, constructs the objective function with the minimum error, continuously and iteratively updates the wolf cluster aiming at the initialized coordinate space of the wolf cluster, and finally outputs the demand response elastic matrix, namely, the gray wolf optimization algorithm is introduced to realize the short-term response load prediction training.
It should be noted that the response load prediction model training for elastic matrix modification based on the gray wolf optimization algorithm includes the following steps:
1) reading electricity price and load data before and after 24h of response every day for M days;
2) the load demand variation Δ q in the i-th day period is calculated according to the following formula (6) and formula (7), respectivelyi(m) and rate of change of electricity price Δ p for each periodi(m)：
Figure BDA0002940941580000121
Figure BDA0002940941580000122
Wherein i is 1,2, …,24, M is 1,2, …, M,
Figure BDA0002940941580000123
3) obtaining the original electricity consumption in the ith time period of M days and the electricity price change rate delta p in all time periods (24 time periods) of the day through the following formula (8)i(m) a product vector qi(M×24)；
Figure BDA0002940941580000124
Wherein the content of the first and second substances,
Figure BDA0002940941580000125
Pj0 mrepresents the electricity rate before the j time period of the m day to participate in the user response of the demand response,
Figure BDA0002940941580000126
indicating the acceptable price of electricity for the user participating in the demand response at the j time period on the m day, i.e. the price of electricity for the user to respond,
Figure BDA0002940941580000127
the product vector is made between the power load demand amount before the response of the power consumer participating in the demand response in the i-th day and the power price change rate of the m-th day in the whole period (24 hours);
4) obtaining the participation demand response variable quantity of the ith time period of M days according to the demand response price elasticity
Figure BDA0002940941580000128
Estimated value of Δ q'i mAnd can be represented by formula (9):
Δq′i m＝e(i，j)·(qi m)T i＝j＝1，2，…，24 (9)
5) the minimum reconstruction error of the i-period participation demand response variation is taken as an objective function, and the objective function can be expressed by a formula (10):
Figure BDA0002940941580000132
6) solving the objective function by adopting a gray wolf optimization algorithm in an intelligent optimization algorithm, and setting the following parameters: d is the number of wolfs, ymaxRepresenting the maximum number of iterations, PVIndicating the probability of directional correction, andi(1×24)the 24 elements of (1) are set as the initialized coordinate space of the wolf group;
7) updating the wolf group position according to rules of surrounding, hunting, updating and the like when the gray wolf is hunted, creating a new generation gray wolf individual, merging the father generation and the generated new offspring through a traditional elite-maintained preferred strategy to form a new wolf group characteristic which is marked as D;
8) judging whether a population iteration end condition y is reachedmaxIf yes, carrying out the next step, if the condition is not met, executing the previous step, and executing the operation of adding 1 for the iteration times;
9) judging whether i is more than or equal to 24, if so, outputting a demand response elastic matrix; if not, the loop is re-entered, the demand price elasticity coefficient of the next time interval is optimized, i +1 operation is executed, and when i is 24, all steps are ended, and the demand response elasticity matrix is output.
Based on any one of the embodiments above, the constructing an objective function with a minimum error according to the response load, the load before and after response, and the electricity price in the sample data includes:
respectively obtaining the load demand variation delta q of the i-th day period of the M day based on the load before and after the 24-time-period response in the M days and the electricity pricei(m) and corresponding rate of change of electricity price Δ pi(m)；
Load demand change based on the m-th day i periodChemical quantity Δ qi(m) and corresponding rate of change of electricity price Δ pi(m) obtaining a vector of products made between the demand amount of the responsive preload participating in the demand response at the i-th day period and the rate of change in the electricity prices at the 24 i-th day period
Figure BDA0002940941580000133
Based on the product vector
Figure BDA0002940941580000134
And demand response price elastic coefficient to obtain the m-th day i time interval participation demand response variable quantity
Figure BDA0002940941580000135
Estimated value of Δ q'i m；
Participating in demand response variation based on the i-th day
Figure BDA0002940941580000137
Estimated value of Δ q'i mAnd the product vector
Figure BDA0002940941580000139
And constructing an error minimum objective function.
Based on any of the above embodiments, the solving the objective function with the minimum error based on the gray wolf optimization algorithm that sets the number of wolf clusters, the maximum number of iterations, the direction correction probability, and the initialized coordinate space of the wolf clusters to obtain the demand response elastic matrix includes:
updating the wolf pack position according to a preset rule to obtain the new wolf pack characteristics formed by the new generation of wolf individuals, and judging whether the maximum iteration times is reached currently;
if the maximum number of iterations is reached, judging whether the 24 time intervals are met, if so, outputting a demand response elastic matrix, otherwise, re-optimizing the demand price elastic coefficient of the next time interval, and outputting the demand response elastic matrix until 24 time intervals are executed;
if the iteration maximum number is not reached, updating the position of the wolf pack according to the preset rule again, adding 1 to the iteration number, and continuously executing the step of updating the position of the wolf pack according to the preset rule until the iteration maximum number is reached.
Specifically, the objective function with the minimum error is solved by using a gray wolf optimization algorithm in an intelligent optimization algorithm, and the following parameters are set: d is the number of wolfs, ymaxRepresenting the maximum number of iterations, PVIndicating the probability of directional correction, andi(1×24)the 24 elements of (1) are set as the initialized coordinate space of the wolf group;
updating the wolf group position according to rules of surrounding, hunting, updating and the like when the gray wolf is hunted, creating a new generation of gray wolf individuals, and combining the father generation and the generated new offspring through a traditional elite-maintained preferred strategy to form a new wolf group characteristic which is marked as D.
Judging whether a population iteration end condition y is reachedmaxIf yes, carrying out the next step, if the condition is not met, executing the previous step, and executing the operation of adding 1 for the iteration times;
judging whether i is more than or equal to 24, if so, outputting a demand response elastic matrix; if not, the loop is re-entered, the demand price elasticity coefficient of the next time interval is optimized, i +1 operation is executed, and when i is 24, all steps are ended, and the demand response elasticity matrix is output.
Based on any of the above embodiments, the updating the wolf pack position according to the predetermined rule to obtain a new generation of wolf individuals to form a new wolf pack characteristic includes: updating the position of the wolf group according to the rules including surrounding, hunting and updating when the gray wolf is hunted, generating a new generation gray wolf individual, and combining the father generation and the generated new offspring preferentially to form a new wolf group characteristic.
Any of the above embodiments of the present invention are described below with particular reference to the following applications:
as shown in FIG. 5, historical basic load data, temperature and humidity data of 24 hours per day of 2016, 6, 7 and 1 months in the load forecasting tournament of A are selected as training sets, and the CS-SVM and other 2 forecasting models provided by the invention are adopted to forecast the 24-hour basic load of 2016, 8, 1 and 1 days in 8 months. In the model of the CS optimization SVM, the parameters are set as follows: n-20, Pa＝0.01、C＝50、σ＝0.05。
The invention adopts indexes of Root Mean Square Error (RMSE), average relative percentage error (MAPE) and relative error percentage (e) to judge the quality of the model prediction result provided by the invention, and the specific calculation formula of each index is as follows:
Figure BDA0002940941580000151
Figure BDA0002940941580000152
Figure BDA0002940941580000153
wherein: x is the number ofiRepresenting the actual load value of the ith hour of the day to be predicted;
Figure BDA0002940941580000154
the predicted load value of the ith hour of the day to be predicted is represented, the number of samples is n, and n is 1,2, …, 24.
The average error values of the 3 predictions of the above 3 base load prediction models for 8 months and 1 day are shown in table 1.
TABLE 1 comparison table of 3-time average prediction errors of each model
Figure BDA0002940941580000155
As can be seen, the prediction errors RMSE and MAPE of the CS-SVM are the smallest, so that the prediction accuracy of the method is the highest.
As shown in FIG. 6, the load before and after response and the electricity price data before and after response of two weeks (14 days) before the prediction day (8 months and 1 day) are selected, and the demand price elasticity and the actual value of the basic load are optimized through the gray wolf algorithm to obtain a comparison graph of the response load prediction curve and the actual load curve. The error ratio of the response load predicted value to the actual value is shown in table 2.
TABLE 2 error comparison table for predicted value and actual value of response load
Figure BDA0002940941580000161
Therefore, the predicted value curve of the active demand response load obtained by the prediction method provided by the invention is compared with the actual value curve and is fit. In Table 2, 9:00 am, the relative error percentage reaches a maximum value of emax32.1157 pm at 8:00 pm, the relative error percentage appears to be the minimum, emin0.7276, the overall error is small and the proposed gray wolf algorithm based on correcting the demand price elasticity response load prediction is effective.
As shown in fig. 7, based on the prediction of the previous base load and the response load, the predicted base load and the response load are superimposed to finally obtain a load prediction curve after response. The error ratio of the predicted value to the actual value of the after-response load is shown in table 3.
TABLE 3 comparison table of error between predicted value and actual value of load after response
Figure BDA0002940941580000171
Figure BDA0002940941580000181
Therefore, the predicted load curve of the power consumer after active response obtained by the prediction method provided by the invention is relatively fitted with the actual load curve, and the predicted value curve of the active demand response load is fitted with the actual value curve. The effectiveness of the proposed method is demonstrated here. In Table 3, the maximum relative error percentage emax-2.0910, occurring in the morning at 7: 00; minimum relative error percentage emin-0.0354, which occurs at 8:00 pm. Error indicator RMSE 6.8935, MAThe PE is 0.95, the overall error is small, and the response load prediction method based on the gray wolf algorithm for correcting the demand price elasticity is effective.
In summary, compared with the prior art, the invention has the beneficial effects that: the combined load prediction method and the combined load prediction device provided by the invention deeply excavate the autocorrelation of the electricity price difference, the load difference, the temperature and humidity sequence and the load, and excavate the relation between the historical value and the value to be predicted by means of the demand-price elasticity and the support vector machine tool in the economics; by carrying out comparative analysis on the CS-SVM, the PSO-SVM, the SVM and the BP prediction model considering the demand price elasticity, the method provided by the invention has the advantages of lower prediction error and higher speed; meanwhile, the prediction error of the CS-SVM prediction model considering the demand price elasticity introduced by the electricity price difference and the load difference is lower than that of the same model not introducing the demand price elasticity, and the prediction method considering the demand price elasticity designed by the invention improves the prediction precision.
The following describes a combined load prediction device provided by the present invention, and the following description and the above-described combined load prediction method may be referred to in correspondence.
Fig. 8 is a schematic structural diagram of a combined load prediction apparatus according to an embodiment of the present invention, as shown in fig. 8, the apparatus includes a data determination unit 810 and a load prediction unit 820;
a data determination unit 810 for determining a value of a forecasted weather factor, a base load, a response load, a load before and after the response, and a power rate for a day to be predicted;
the load prediction unit 820 is used for inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be predicted into a prediction model to obtain a power grid load prediction value output by the prediction model;
the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days;
the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
According to the device provided by the embodiment of the invention, the prediction model is trained on the basis of the actual meteorological factor values, the basic load, the response load, the loads before and after response and the sample data of the electricity price on a plurality of historical days, and the power grid load prediction value on the day to be predicted is obtained by inputting the forecast meteorological factor values, the basic load, the response load, the loads before and after response and the electricity price on the day to be predicted into the prediction model, so that the load participating in demand response in the power grid system can be accurately predicted.
Based on any of the above embodiments, the load prediction unit 820 includes a prediction model composed of a basic load prediction module, a response load prediction module, and a superposition module;
the basic load prediction module is used for inputting the value of the forecast environment factor of the day to be predicted and the basic load and outputting a basic load prediction value;
the response load prediction module is used for inputting the response load of the day to be predicted, the load before and after response and the electricity price and outputting a predicted value of the demand response load;
and the superposition model is used for inputting the basic load predicted value and the demand response load predicted value and outputting the power grid load predicted value of the day to be predicted.
Based on any one of the embodiments, the basic load prediction module is obtained by performing short-term basic load prediction training on the optimization module based on the actual meteorological factor values of the plurality of historical days and sample data of basic loads;
the optimization module comprises a CS-SVM module, a prediction error module, a position iteration module and a parameter updating module;
the CS-SVM module is used for inputting the value of an actual meteorological factor and a basic load in the sample data and outputting the optimal fitness value of the bird nest after setting initial parameters including a penalty coefficient, the width of a radial basis kernel function, an initial probability, the bird nest position and the maximum iteration frequency;
the prediction error module is used for determining the current optimal bird nest position according to the optimal fitness value of the bird nest, updating other bird nest positions according to the current optimal bird nest position to generate a group of new bird nest positions, and comparing the current optimal bird nest position with the group of new bird nest positions to obtain a prediction error;
the position iteration module is used for obtaining a better bird nest position according to the prediction error, comparing the probability of eliminating cuffed bird eggs with the numerical value of the random number, replacing the current optimal bird nest position with a group of new bird nest positions when the probability of eliminating cuffed bird eggs is smaller than the random number, judging whether the current iteration end condition is met, jumping out to find the value of the optimal bird nest position if the current iteration end condition is met, and continuously searching the optimal bird nest position if the current iteration end condition is not met; wherein the iteration end condition comprises a maximum iteration number or an optimal fitness value determined by the width of a radial basis kernel function;
and the parameter updating module is used for establishing a basic load prediction model for the optimal bird nest position based on parameter updating in the CS-SVM model of the cuckoo optimization support vector machine, and completing short-term basic load prediction training.
Based on any one of the embodiments, the response load prediction module is obtained by performing short-term response load prediction training on a target module based on the response loads of the plurality of historical days, the sample data of the loads before and after response and the sample data of the electricity prices;
the target module comprises a target function module, an optimization algorithm module and a model construction module;
the objective function module is used for constructing an objective function with the minimum error according to the response load, the load before and after response and the electricity price in the sample data;
the optimization algorithm module is used for solving the objective function with the minimum error based on a gray wolf optimization algorithm for setting the number of wolf clusters, the maximum number of iterations, the direction correction probability and the initialized coordinate space of the wolf clusters to obtain a demand response elastic matrix;
and the model building module is used for building the response load prediction model based on the demand response elastic matrix and completing short-term response load prediction training.
Based on any one of the embodiments above, the constructing an objective function with a minimum error according to the response load, the load before and after response, and the electricity price in the sample data includes:
respectively obtaining the load demand variation delta q of the i-th day period of the M day based on the load before and after the 24-time-period response in the M days and the electricity pricei(m) and corresponding rate of change of electricity price Δ pi(m)；
A load demand variation Δ q based on the m-th day i periodi(m) and corresponding rate of change of electricity price Δ pi(m) obtaining a vector of products made between the demand amount of the responsive preload participating in the demand response at the i-th day period and the rate of change in the electricity prices at the 24 i-th day period
Figure BDA0002940941580000211
Based on the product vector
Figure BDA0002940941580000212
And demand response price elastic coefficient to obtain the m-th day i time interval participation demand response variable quantity
Figure BDA0002940941580000213
Estimated value of Δ q'i m；
Participating in demand response variation based on the i-th day
Figure BDA0002940941580000215
Estimated value of Δ q'i mAnd the product vector
Figure BDA0002940941580000217
And constructing an error minimum objective function.
Based on any of the above embodiments, the solving the objective function with the minimum error based on the gray wolf optimization algorithm that sets the number of wolf clusters, the maximum number of iterations, the direction correction probability, and the initialized coordinate space of the wolf clusters to obtain the demand response elastic matrix includes:
updating the wolf pack position according to a preset rule to obtain the new wolf pack characteristics formed by the new generation of wolf individuals, and judging whether the maximum iteration times is reached currently;
if the maximum number of iterations is reached, judging whether the 24 time intervals are met, if so, outputting a demand response elastic matrix, otherwise, re-optimizing the demand price elastic coefficient of the next time interval, and outputting the demand response elastic matrix until 24 time intervals are executed;
if the iteration maximum number is not reached, updating the position of the wolf pack according to the preset rule again, adding 1 to the iteration number, and continuously executing the step of updating the position of the wolf pack according to the preset rule until the iteration maximum number is reached.
Based on any of the above embodiments, the updating the wolf pack position according to the predetermined rule to obtain a new generation of wolf individuals to form a new wolf pack characteristic includes: updating the position of the wolf group according to the rules including surrounding, hunting and updating when the gray wolf is hunted, generating a new generation gray wolf individual, and combining the father generation and the generated new offspring preferentially to form a new wolf group characteristic.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 9, the electronic device may include: a processor (processor)910, a communication Interface (Communications Interface)920, a memory (memory)930, and a communication bus 940, wherein the processor 910, the communication Interface 920, and the memory 930 communicate with each other via the communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform a combined load prediction method comprising: determining the value of a forecast meteorological factor, a basic load, a response load, a load before and after response and an electricity price of a day to be predicted; inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model; the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days; the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
Furthermore, the logic instructions in the memory 930 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the combined load prediction method provided by the above methods, where the method includes: determining the value of a forecast meteorological factor, a basic load, a response load, a load before and after response and an electricity price of a day to be predicted; inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model; the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days; the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
In yet another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the combined load prediction method provided in the foregoing, the method including: determining the value of a forecast meteorological factor, a basic load, a response load, a load before and after response and an electricity price of a day to be predicted; inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model; the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days; the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A combined load prediction method, comprising:
determining the value of a forecast meteorological factor, a basic load, a response load, a load before and after response and an electricity price of a day to be predicted;
inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model;
the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days;
the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
2. The combined load prediction method of claim 1, wherein the prediction models comprise a base load prediction model, a responsive load prediction model, and a superposition model;
inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be forecasted into a forecasting model to obtain a power grid load forecasting value output by the forecasting model, wherein the power grid load forecasting value comprises the following steps:
inputting the value of the forecast environment factor of the day to be forecasted and the basic load into the basic load forecasting model, and outputting a basic load forecasting value;
inputting the response load of the day to be predicted, the load before and after response and the electricity price into the response load prediction model, and outputting a predicted value of the demand response load;
and inputting the basic load predicted value and the demand response load predicted value into the superposition model, and outputting a power grid load predicted value of a day to be predicted.
3. The combined load forecasting method according to claim 2, wherein the base load forecasting model is obtained by performing short-term base load forecasting training based on the values of the actual meteorological factors and sample data of the base load on the plurality of historical days;
and completing short-term base load prediction training based on the actual meteorological factor value and the base load in the sample data, wherein the method comprises the following steps:
s1, inputting the actual meteorological factor value and the basic load in the sample data into a CS-SVM (support vector machine-support vector machine) model of the brook optimization support vector machine set by initial parameters including a penalty coefficient, the width of a radial basis kernel function, an initial probability, a nest position and the maximum iteration number to obtain the optimal fitness value of the nest;
s2, determining the current optimal bird nest position according to the optimal fitness value of the bird nest, updating other bird nest positions according to the current optimal bird nest position to generate a group of new bird nest positions, and comparing the current optimal bird nest position with the group of new bird nest positions to obtain a prediction error;
s3, obtaining a better bird nest position according to the prediction error, comparing the probability of eliminating cuckoo eggs with the numerical value of the random number, replacing the current optimal bird nest position with a group of new bird nest positions when the probability of eliminating cuckoo eggs is smaller than the random number, judging whether the current iteration end condition is met, jumping out to find the value of the optimal bird nest position if the current iteration end condition is met, and returning to S2 to continuously find the optimal bird nest position if the current iteration end condition is not met; wherein the iteration end condition comprises a maximum iteration number or an optimal fitness value determined by the width of a radial basis kernel function;
and S4, updating parameters in the CS-SVM model of the Cuckoo optimization support vector machine to establish a basic load prediction model for the optimal bird nest position, and completing short-term basic load prediction training.
4. The combined load forecasting method according to claim 2, wherein the response load forecasting model is obtained by performing short-term response load forecasting training based on sample data of response loads, loads before and after response and electricity prices of the plurality of historical days;
completing short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data, and comprising the following steps:
constructing an error minimum objective function according to the response load, the load before and after response and the electricity price in the sample data;
solving the target function with the minimum error based on a gray wolf optimization algorithm for setting the number of wolf clusters, the maximum number of iterations, the direction correction probability and the initialized coordinate space of the wolf clusters to obtain a demand response elastic matrix;
and establishing the response load prediction model based on the demand response elastic matrix to finish short-term response load prediction training.
5. The combined load prediction method according to claim 4, wherein the constructing an objective function with minimum error according to the response load, the load before and after response and the electricity price in the sample data comprises:
respectively obtaining the load demand variation delta q of the i-th day period of the M day based on the load before and after the 24-time-period response in the M days and the electricity pricei(m) and corresponding rate of change of electricity price Δ pi(m)；
A load demand variation Δ q based on the m-th day i periodi(m) and corresponding rate of change of electricity price Δ pi(m) obtaining a vector of products made between the demand amount of the responsive preload participating in the demand response at the i-th day period and the rate of change in the electricity prices at the 24 i-th day period
Figure FDA0002940941570000031
Based on the product vector
Figure FDA0002940941570000032
And demand response price elastic coefficient to obtain the m-th day i time interval participation demand response variable quantity
Figure FDA0002940941570000033
Is estimated value of
Figure FDA0002940941570000034
Participating in demand response variation based on the i-th day
Figure FDA0002940941570000035
Is estimated value of
Figure FDA0002940941570000036
And the product vector
Figure FDA0002940941570000037
And constructing an error minimum objective function.
6. The combined load prediction method of claim 4, wherein the solving the error minimization objective function based on the grayling optimization algorithm that sets the number of wolf clusters, the maximum number of iterations, the directional correction probability, and the initialized coordinate space of wolf clusters to obtain the demand response elastic matrix comprises:
updating the wolf pack position according to a preset rule to obtain the new wolf pack characteristics formed by the new generation of wolf individuals, and judging whether the maximum iteration times is reached currently;
if the maximum number of iterations is reached, judging whether the 24 time intervals are met, if so, outputting a demand response elastic matrix, otherwise, re-optimizing the demand price elastic coefficient of the next time interval, and outputting the demand response elastic matrix until 24 time intervals are executed;
if the iteration maximum number is not reached, updating the position of the wolf pack according to the preset rule again, adding 1 to the iteration number, and continuously executing the step of updating the position of the wolf pack according to the preset rule until the iteration maximum number is reached.
7. The combined load prediction method of claim 6, wherein the updating the wolf pack location according to the predetermined rule to obtain a new generation of wolf individuals forming a new wolf pack characteristics comprises: updating the position of the wolf group according to rules including surrounding, hunting and updating when the gray wolf is hunted, generating a new generation gray wolf individual, and combining the father generation and the generated new offspring generation through optimization to form a new wolf group characteristic.
8. A combined load prediction device, comprising:
the data determining unit is used for determining the value of the forecast meteorological factor, the basic load, the response load, the load before and after the response and the electricity price of the day to be predicted;
the load prediction unit is used for inputting the value of the forecast meteorological factor, the basic load, the response load, the load before and after response and the electricity price of the day to be predicted into a prediction model to obtain a power grid load prediction value output by the prediction model;
the prediction model is obtained by training sample data of actual meteorological factor values, basic loads, response loads, loads before and after response and electricity prices of a plurality of historical days;
the prediction model is used for completing short-term basic load prediction training based on the value of the actual meteorological factor and the basic load in the sample data, and predicting the power grid load of the day to be predicted after completing the short-term response load prediction training based on the response load, the load before and after response and the electricity price in the sample data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the combined load prediction method according to any of claims 1 to 7 are implemented when the program is executed by the processor.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the combined load prediction method according to any one of claims 1 to 7.
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