CN113468817B - Ultra-short-term wind power prediction method based on IGOA (insulated gate bipolar transistor) optimized ELM (ELM) - Google Patents
Ultra-short-term wind power prediction method based on IGOA (insulated gate bipolar transistor) optimized ELM (ELM) Download PDFInfo
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Abstract
The invention discloses an ultra-short-term wind power prediction method based on IGOA (insulated gate bipolar transistor) optimization ELM, which comprises the following steps: acquiring historical wind speed and wind power data from a wind power plant data acquisition and monitoring control system, preprocessing the data, selecting a training data sample and a test data sample, and establishing an ELM prediction model; improving GOA, and initializing the population of GOA by adopting a cube chaotic mapping method; updating the decremental coefficients by a sine function based adjustment parameter control strategy; introducing cauchy mutation operation at the position updating position of the locust individual to obtain IGOA; optimizing parameters of the ELM by using the IGOA to obtain optimal parameters; inputting the obtained optimal parameters and test data samples into an ELM prediction model to obtain a prediction result, and selecting three commonly used evaluation indexes in wind power prediction: the prediction performance of the model is evaluated by the root mean square error RMSE, the average absolute error MAE and the maximum absolute error Emax. The method effectively predicts the ultra-short-term wind power and improves the precision of the prediction model.
Description
Technical Field
The invention relates to the field of wind power prediction, in particular to an ultra-short-term wind power prediction method based on IGOA (insulated gate bipolar transistor) optimization ELM (electronic dynamic range).
Background
With the increasing maturity of wind power generation technology, the capacity of a single wind power unit and the scale of a grid-connected wind power plant are continuously enlarged, and the proportion of wind power to the total power generation amount of a power system is increased year by year. The penetrating power of the wind power plant is continuously increased, a series of problems brought to the power system are increasingly prominent, and the serious power system and the power system are safe, stable, economical and reliable to operate. The wind power is predicted timely and accurately, so that the safety, stability, economy and controllability of the power system can be obviously enhanced.
The extreme learning machine (Extreme Learning Machine, ELM) is used to train a single hidden layer feedforward neural network (SLFN) that, unlike conventional SLFN training algorithms, randomly generates input layer weights and hidden layer biases, and in training, only the number of hidden layer neurons needs to be set to obtain a unique optimal solution without adjustment. However, since the gradient descent method requires multiple iterations to achieve the purpose of correcting the weight and the threshold, the training process takes a long time and is easy to sink into local minima. For this reason, a more efficient optimization algorithm is needed to improve the prediction accuracy of the extreme learning machine.
The locust algorithm (Grasshopper Optimization Algorithm, GOA) is a meta-heuristic bionic optimization algorithm proposed by the year 2017 by Saremi, but is easy to sink into local extreme points in the later stage of the algorithm, the convergence speed is slow, and the prediction accuracy is reduced. The method comprises the steps of introducing a cube chaotic mapping method, updating a decreasing coefficient through a sine function-based adjustment parameter control strategy, introducing a cauchy variation operation at the position update position of an individual locust to improve a traditional locust algorithm, optimizing an extreme learning machine by utilizing the improved locust algorithm, acquiring optimal parameters, establishing a new extreme learning machine prediction model, and realizing ultra-short-term wind power prediction.
Disclosure of Invention
The invention aims to: the invention aims to solve the technical problems that parameters are difficult to determine, local optimum is easy to fall into and the like in the existing ELM prediction model, and improves the accuracy of the prediction model.
The technical scheme is as follows: an ultra-short-term wind power prediction method based on IGOA (insulated gate bipolar transistor) optimization ELM (ELM) comprises the following steps:
step 1: acquiring historical wind speed and wind power data from a wind power plant data acquisition and monitoring control system, preprocessing the data, selecting a training data sample and a test data sample, and establishing an ELM prediction model;
step 2: improving GOA, and initializing the population of GOA by adopting a cube chaotic mapping method;
step 3: updating the decremental coefficients by a sine function based adjustment parameter control strategy;
step 4: introducing cauchy mutation operation at the position updating position of the locust individual to obtain IGOA;
step 5: optimizing parameters of the ELM by using the IGOA to obtain optimal parameters;
step 6: inputting the obtained optimal parameters and test data samples into an ELM prediction model to obtain a prediction result, and selecting three commonly used evaluation indexes in wind power prediction: root mean square error RMSE, mean absolute error MAE and maximum absolute error E max And evaluating the prediction performance of the model.
Further, the preprocessing of the data in the step 1 specifically includes:
step 1.1.1: if the collected power data is larger than the wind power installation capacity, replacing the collected power data with the wind power installation capacity, and if the collected power data is negative, replacing the collected power data with zero;
step 1.1.2: for the missing data between adjacent moments, replacing the missing data by an average value of the two adjacent data, and for the abnormal data, replacing the data at the previous moment;
step 1.1.3: normalization processing is performed on the data according to the formula (1):
wherein: y is normalized data, x is raw data, x max And x min Respectively the maximum value and the minimum value in the original data, and the normalization range is 0,1]。
Further, the building of the ELM prediction model in step 1 specifically includes:
step 1.2.1: training data samplesWherein x is i And t i For training data true values, N is the number of nodes of an input layer, N and m are vector space dimensions, and an activation function g (■) of an implicit layer and the number of nodes L of the implicit layer are input into an algorithm model;
step 1.2.2: determining input layer weights w i ∈[-1,1]And hidden layer bias b i ∈[0,1]；
Step 1.2.3: the output matrix H of the hidden layer is calculated according to equation (2),
wherein H= { H 1 ,h 2 ,…,h i }，h i ＝g(w i x i +b i ) Is the output of the hidden layer i-th neuron.
Step 1.2.4: solving an output weight beta, and obtaining the output weight beta through least square solution, namely the formula (3):
wherein: h is the generalized inverse of Moore-Penrose of matrix H;
step 1.2.5: ELM prediction model is obtained by the output weight, namely (4):
wherein: beta i For the connection weight between the i node of the hidden layer and the output layer, w i B, for the connection weight between the input layer and the i node of the hidden layer i Is the bias of the hidden layer i-th node.
Further, step 2 specifically includes:
step 2.1: individuals in a plurality of locust populations are arranged in the D-dimensional space, and the individuals in the locust populations are generated through a cube chaotic mapping formula (5), namely Y= (Y) 1 ,y 2 ,...,y n ) And the value range is y n ∈(0,1)；
Wherein a is a control parameter.
Step 2.2: iterating each dimension of the individual of the locust population N-1 times, thereby generating N-1 individuals of the artificial locust population;
step 2.3: when the individual iteration is completed in all artificial locust populations, mapping into solution space according to formula (6):
wherein: n is locust population number, ub d 、lb d For the upper and lower bounds of the search space,is the d-th dimensional coordinate of the individual of the ith artificial locust population,/for>Is the d-th dimension of the individual of the ith artificial locust population.
Further, in step 2.1, the chaos of the Cubic mapping is related to the value of the control parameter, and when a=2.6, the Cubic mapping has better chaos traversal.
Further, the step 3 specifically includes:
the decreasing coefficient c of the traditional locust algorithm is used for balancing the effects of global search and local development and is proportional to the iteration times, but cannot meet the actual requirements of the algorithm in different periods: the decreasing coefficient c is required to be larger and slowly decreased in the early stage of the algorithm, and the method has the capability and time of carrying out global search to reach the approximate range of the global optimal solution; the decreasing coefficient c is required to be smaller and rapidly decreased in the later stage of the algorithm, the algorithm is rapidly converged to a local optimal solution, and the control strategy of the adjustment parameters based on the sine function is shown as a formula (7):
in the formula, T is the current iteration number of the algorithm, T is the maximum iteration number, and k and u are adjustment parameters.
Further, k= 2,u =2 is selected as the adjustment parameter.
Further, the step 4 specifically includes:
in the traditional algorithm, locust individuals update the position of the next step according to the distance between the current individual and other individuals in the population and the position of the optimal individual, and the problem that the locusts are prone to being in local optimal in the later period is solved. According to the cauchy standard distribution function formula (8), a cauchy variation formula (9) is constructed for the optimal individual, and the position updating formula of the locustus individual after variation is shown as (10):
wherein:for the algorithm iteration to the position of the ith locust in the d-th dimension at the t-th time, ++>For the algorithm to iterate to the position of the jth locust in the d dimension at the t time, d ij (t) is the distance between the ith and the jth locust of the locust population at the t iteration, ++>Is the current optimal individual position,/->The optimal locust position after Cauchy mutation is that r is a random number on (0, 1), p is mutation probability, and p=0.7 is selected through experiments.
Further, step 5 specifically includes:
step 5.1: initializing related algorithm parameters: setting locust population N, space dimension D, maximum iteration number T, upper bound ub and lower bound lb of search space and current iteration number t=1, and initializing to generate the space position of current locust by adopting chaos theory
Step 5.2: calculating the predicted value y trained by ELM network through a formula (11) i And training sample actual value t i As the root mean square error of the locust fitness value fitness, saving the locust position with the minimum current fitness value to a variable
Step 5.3: judging whether the current iteration times T reach the maximum iteration times T or not; if yes, executing the step 5.5, otherwise executing the step 5.4;
step 5.4: the decreasing coefficient c is updated by adjusting the parameter control strategy (7), the distance between locusts is standardized to be [1,4 ]]Updating the locusts by the cauchy variation formulas (9) and (10), returning the current iteration times t=t+1, returning to ELM network training to obtain a new predicted value, updating the fitness value of the locusts according to the formula (11), comparing with the current optimal solution, and updating if the fitness value is superior to the current optimal solutionOtherwise, executing the step 5.3;
step 5.5: outputting the optimal solutionFrom which the input layer weights w and hidden layer biases b required by the ELM network are extracted.
Further, in step 6, RMSE, MAE and E max The three evaluation index formulas are as follows:
E max ＝max(|P ti -P yi |) (14)
wherein: n is the number of samples, P ti For the actual power at time i, P yi For the predicted power at time i, P cap And starting up the total capacity for the wind farm.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: firstly, initializing the position of a locust by adopting a cube chaotic mapping method for GOAThe method comprises the steps of constructing a decreasing coefficient c of an adjustment parameter control strategy based on a sine function and adding a cauchy mutation operation at a position update position of a locust, so that the mutated locust can increase population diversity, quicken convergence speed, help an algorithm jump out of local optimum, find a new optimum solution and enhance algorithm reliability; and then, the parameters of the ELM are optimized by utilizing the IGOA, so that the aim of improving the accuracy of the prediction model is fulfilled.
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FIG. 1 is a flow chart of an ultra-short-term wind power prediction method based on IGOA optimization ELM.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Aiming at the defects of the prior art, the method comprises the steps of initializing the position of a locust by adopting a cube chaotic mapping method for GOA, constructing a decreasing coefficient c of an adjustment parameter control strategy based on a sine function, adding a Cauchy mutation operation at the position updating position of an individual locust, enabling the mutated locust to increase population diversity, accelerating convergence speed, helping an algorithm to jump out of local optimum, finding a new optimal solution, enhancing the reliability of the algorithm, optimizing the parameters of ELM by utilizing IGOA, and establishing a new ELM prediction model.
In the invention, the actual measurement data of a wind farm 2021, which is continuously measured for 250 hours in 5 months, is taken as an embodiment, and example simulation is carried out to verify the effect of the invention. FIG. 1 is a flowchart of an ultra-short-term wind power prediction model based on IGOA optimization ELM, and the implementation steps are as follows:
step 1: historical wind speed and wind power data with sampling time resolution of 10min, which are continuously 250h for 5 months, are obtained from a wind power plant data acquisition and monitoring control system, are subjected to a series of preprocessing, training data samples and test data samples are selected, and an ELM prediction model is established.
Wherein performing a series of pre-processing of the data comprises:
step 1.1.1: if the collected power data is larger than the wind power installation capacity, replacing the collected power data with the wind power installation capacity, and if the collected power data is negative, replacing the collected power data with zero;
step 1.1.2: for the missing data between adjacent moments, replacing the missing data by an average value of the two adjacent data, and for the abnormal data, replacing the data at the previous moment;
step 1.1.3: in order to facilitate data processing, the data is normalized according to formula (1).
Wherein: y is normalized data, x is raw data, x max And x min Respectively the maximum value and the minimum value in the original data, and the normalization range is 0,1]。
The building of the ELM prediction model includes:
step 1.2.1: training data samplesx i And t i For training data true values, N is the number of nodes of an input layer, N and m are vector space dimensions, and an activation function g (■) of an implicit layer and the number of nodes L of the implicit layer are input into an algorithm model;
step 1.2.2: determining input layer weights w i ∈[-1,1]And hidden layer bias b i ∈[0,1]；
Step 1.2.3: calculating an output matrix H of the hidden layer, i.e. equation (2):
wherein H= { H 1 ,h 2 ,…,h i }，h i ＝g(w i x i +b i ) Is the output of the hidden layer i-th neuron.
Step 1.2.4: solving the output weight beta, which can be obtained by least squares solution, namely formula (3):
wherein: h is the generalized inverse of Moore-Penrose of matrix H;
step 1.2.5: ELM prediction model is obtained by the output weight, namely (4):
wherein: beta i For the connection weight between the i node of the hidden layer and the output layer, w i B, for the connection weight between the input layer and the i node of the hidden layer i Is the bias of the hidden layer i-th node.
Step two: the GOA is improved, and the population of the GOA is initialized by adopting a cube chaotic mapping method.
The method for initializing the population of the GOA by the cube chaotic mapping method comprises the following steps:
step 2.1: individuals in a plurality of locust populations are arranged in the D-dimensional space, and the individuals in the locust populations are generated through a cube chaotic mapping formula (5), namely Y= (Y) 1 ,y 2 ,...,y n ) And the value range is y n ∈(0,1)；
Wherein a is a control parameter; the chaos of the cube map is related to the value of the parameter a, and through looking up related data and experiments, when a=2.6 is selected, the cube map has better chaos ergodic property.
Step 2.2: then iterating each dimension of the individuals of the locust population for N-1 times, thereby generating N-1 individuals of the artificial locust population;
step 2.3: finally, when the iteration of the individuals in all artificial locust populations is completed, mapping the artificial locust populations into a solution space according to a formula (6):
in the above formula: n is locust population number, ub d 、lb d For the upper and lower bounds of the search space,is the d-th dimensional coordinate of the individual of the ith artificial locust population,/for>Is the d-th dimension of the individual of the ith artificial locust population.
Step three: the decrementing coefficients are then updated by a sine function based tuning parameter control strategy.
The decreasing coefficient c of the traditional locust algorithm is used for balancing the effects of global search and local development and is proportional to the iteration times, but cannot meet the actual requirements of the algorithm in different periods: the decreasing coefficient c is required to be larger and slowly decreased in the early stage of the algorithm, so that the algorithm has enough capacity and time to perform global searching to reach the approximate range of the global optimal solution; the decrease coefficient c is required to be smaller and decrease rapidly in the later stage of the algorithm, so that the algorithm can be quickly converged to the local optimal solution. For this purpose, a sine function-based adjustment parameter control strategy is provided, and the formula is shown as (7):
in the formula, T is the current iteration number of the algorithm, T is the maximum iteration number, k and u are adjustment parameters, and k= 2,u =2 is selected by the invention through experiments.
Step four: and finally, introducing a cauchy mutation operation at the position update position of the locust individual.
In the traditional algorithm, locust individuals update the position of the next step according to the distance between the current individual and other individuals in the population and the position of the optimal individual, and the problem that the locusts are prone to being in local optimal in the later period is solved. According to the cauchy standard distribution function formula (8), a cauchy variation formula (9) is constructed for the optimal individual, and the position updating formula of the locustus individual after variation is shown as (10):
wherein:for the algorithm iteration to the position of the ith locust in the d-th dimension at the t-th time, ++>For the algorithm to iterate to the position of the jth locust in the d dimension at the t time, d ij (t) is the distance between the ith and the jth locust of the locust population at the t iteration, ++>Is the current optimal individual position,/->The optimal locust position after Cauchy mutation is that r is a random number on (0, 1), p is mutation probability, and p=0.7 is selected through experiments.
Step five: and optimizing the parameters of the ELM by using the IGOA to obtain the optimal parameters.
Wherein optimizing parameters of the ELM using the IGOA, obtaining optimal parameters comprises:
step 5.1: initializing related algorithm parameters: setting locust population N, space dimension D, maximum iteration number T, upper bound ub and lower bound lb of search space and current iteration number t=1, and initializing to generate the space position of current locust by adopting chaos theory
Step 5.2: calculating the predicted value y trained by ELM network through a formula (11) i And training sample actual value t i As the root mean square error of the locust fitness value fitness, and saving the locust position with the smallest current fitness value to the variable
Step 5.3: and judging whether the current iteration times T reach the maximum iteration times T or not. If yes, executing step S5, otherwise executing step S4;
step 5.4: updating the decremental coefficient c by adjusting the parameter control strategy formula (7), normalizing the distance between locusts to [1,4 ]]Updating the locusts by the cauchy variation formulas (9) and (10), returning the current iteration times t=t+1, returning to ELM network training to obtain a new predicted value, updating the fitness value of the locusts according to the formula (11), comparing with the current optimal solution, and updating if the fitness value is superior to the current optimal solutionOtherwise, executing the step S3;
step 5.5: outputting the optimal solutionAnd extracts therefrom the input layer weights w and hidden layer biases b required by the ELM network.
Step (a)Sixth,: inputting the obtained optimal parameters and test data samples into an ELM prediction model to obtain a prediction result, and selecting three commonly used evaluation indexes in wind power prediction: root mean square error RMSE, mean absolute error MAE and maximum absolute error E max And evaluating the prediction performance of the model.
Wherein RMSE, MAE and E max The three evaluation index formulas are (12) to (14):
E max ＝max(|P ti -P yi |) (14)
wherein: n is the number of samples, P ti For the actual power at time i, P yi For the predicted power at time i, P cap And starting up the total capacity for the wind farm.
After preprocessing the acquired data, 1050 samples of the first 175h were used for training, 450 samples of the last 75h were used for prediction, and error evaluation analysis was performed on the unoptimized ELM, GOA-ELM, and IGOA-ELM, respectively, and the results are shown in table 1:
TABLE 1
As can be seen from Table 1, the RMSE, MAE and E of the IGOA-ELM model and the GOA-ELM model max The prediction effect is better than that of the non-optimized ELM model, the prediction precision of the ELM model is improved, and the ultra-short-term wind power is effectively predicted.
Aiming at the technical problems that parameters are difficult to determine, local optimum is easy to fall into and the like of the existing ELM prediction model, firstly, a method of cube chaotic mapping is adopted for GOA to initialize the position of a locust, a decreasing coefficient c of a parameter control strategy is adjusted based on a sine function is constructed, and a Cauchy variation operation is added at the position update position of an individual locust, so that the population diversity of the mutated locust can be increased, the convergence speed is accelerated, the algorithm is helped to jump out of local optimum, a new optimal solution is found, the reliability of the algorithm is enhanced, then the parameters of the ELM are optimized by using IGOA, a new ELM prediction model is built, and the ultra-short-term wind power is effectively predicted by taking wind power prediction as an example, so that the effect of the invention is verified.
What is not elaborated on the invention belongs to the prior art which is known to the person skilled in the art.
Claims (7)
1. An ultra-short-term wind power prediction method based on IGOA (insulated gate bipolar transistor) optimization ELM (ELM) is characterized by comprising the following steps:
step 1: acquiring historical wind speed and wind power data from a wind power plant data acquisition and monitoring control system, preprocessing the data, selecting a training data sample and a test data sample, and establishing an ELM prediction model;
the preprocessing of the data in the step 1 specifically includes:
step 1.1.1: if the collected power data is larger than the wind power installation capacity, replacing the collected power data with the wind power installation capacity, and if the collected power data is negative, replacing the collected power data with zero;
step 1.1.2: for the missing data between adjacent moments, replacing the missing data by an average value of the two adjacent data, and for the abnormal data, replacing the data at the previous moment;
step 1.1.3: normalization processing is performed on the data according to the formula (1):
wherein: y is normalized data, x is raw data, x max And x min Respectively the maximum value and the minimum value in the original data, and the normalization range is 0,1]；
Step 2: improving GOA, and initializing the population of GOA by adopting a cube chaotic mapping method;
step 3: updating the decremental coefficients by means of an adjustment parameter control strategy based on a sinusoidal function;
the step 3 specifically includes:
the descending of the decreasing coefficient c is slow at the early stage of the algorithm, and the approximate range of the global optimal solution can be searched globally; the decreasing coefficient c rapidly decreases in the later stage of the algorithm, the algorithm rapidly converges to a local optimal solution, and the control strategy of the adjustment parameters based on the sine function is shown in a formula (2):
in the formula, T is the current iteration times of the algorithm, T is the maximum iteration times, and k and u are adjustment parameters;
step 4: introducing cauchy mutation operation at the position updating position of the locust individual to obtain IGOA;
step 5: optimizing parameters of the ELM by using the IGOA to obtain optimal parameters;
the step 5 specifically includes:
step 5.1: initializing related algorithm parameters: setting locust population N, space dimension D, maximum iteration number T, upper bound ub and lower bound lb of search space and current iteration number t=1, and initializing to generate the space position of current locust by adopting chaos theory
Step 5.2: calculating a predicted value y trained by ELM network through a formula (3) i And training sample actual value t i As the root mean square error of the locust fitness value fitness, saving the locust position with the minimum current fitness value to a variable
Step 5.3: judging whether the current iteration times T reach the maximum iteration times T or not; if yes, executing the step 5.5, otherwise executing the step 5.4;
step 5.4: updating the decremental coefficient c by adjusting the parameter control strategy formula (2), normalizing the distance between locusts to [1,4 ]]Updating the position of the locust by the cauchy variation formula, returning the current iteration times t=t+1, obtaining a new predicted value by ELM network training, updating the fitness value of the locust according to the formula (3), comparing with the current optimal solution, and updating if the fitness value is superior to the current optimal solutionOtherwise, executing the step 5.3;
step 5.5: outputting the optimal solutionExtracting an input layer weight w and an hidden layer bias b required by the ELM network from the ELM network;
step 6: inputting the obtained optimal parameters and test data samples into an ELM prediction model to obtain a prediction result, and selecting three commonly used evaluation indexes in wind power prediction: root mean square error RMSE, mean absolute error MAE and maximum absolute error E max And evaluating the prediction performance of the model.
2. The ultra-short-term wind power prediction method based on the IGOA optimization ELM according to claim 1, wherein the building of the ELM prediction model in the step 1 specifically comprises:
step 1.2.1: training data samplesWherein x is i And t i For training the true value of the data, N is the number of nodes of an input layer, N and m are vector space dimensions, an activation function g (■) of an implicit layer and the number L of nodes of the implicit layer are input into an algorithm moduleIn the form;
step 1.2.2: determining input layer weights w i ∈[-1,1]And hidden layer bias b i ∈[0,1]；
Step 1.2.3: the output matrix H of the hidden layer is calculated according to equation (4),
wherein H= { H 1 ,h 2 ,…,h i }，h i ＝g(w i x i +b i ) The output of the ith neuron which is the hidden layer;
step 1.2.4: solving an output weight beta, and obtaining the output weight beta through least square solution, namely the formula (5):
wherein: h is the generalized inverse of Moore-Penrose of matrix H;
step 1.2.5: ELM prediction model is obtained by the output weight, namely (6):
wherein: beta i For the connection weight between the i node of the hidden layer and the output layer, w i B, for the connection weight between the input layer and the i node of the hidden layer i Is the bias of the hidden layer i-th node.
3. The ultra-short-term wind power prediction method based on the IGOA optimization ELM according to claim 1, wherein the step 2 specifically comprises:
step 2.1: individuals in a plurality of locust populations are arranged in the D-dimensional space, and the individuals in the locust populations are generated through a cube chaotic mapping formula (7), namely Y= (Y) 1 ,y 2 ,...,y n ) And (2) andthe value range is y n ∈(0,1)；
Wherein a is a control parameter;
step 2.2: iterating each dimension of the individual of the locust population N-1 times, thereby generating N-1 individuals of the artificial locust population;
step 2.3: when the individual iteration is completed in all artificial locust populations, mapping into solution space according to equation (8):
wherein: n is locust population number, ub d 、lb d For the upper and lower bounds of the search space,is the d-th dimensional coordinate of the individual of the ith artificial locust population,/for>Is the d-th dimension of the individual of the ith artificial locust population.
4. The ultra-short-term wind power prediction method based on the IGOA-optimized ELM as set forth in claim 3, wherein in the step 2.1, the chaos of the cube map is related to the value of the control parameter, and when a=2.6, the cube map has chaos traversal.
5. The ultra-short term wind power prediction method based on the IGOA optimization ELM of claim 1, wherein the adjustment parameter is k= 2,u =2.
6. The ultra-short-term wind power prediction method based on the IGOA optimization ELM according to claim 1, wherein the step 4 specifically comprises:
adding cauchy mutation operation at the position updating position of the locust individual, increasing population diversity of the mutated locust, and constructing a cauchy mutation formula (10) for the optimal individual according to a cauchy standard distribution function formula (9), wherein the position updating formula of the mutated locust individual is shown as (11):
wherein:for the algorithm iteration to the position of the ith locust in the d-th dimension at the t-th time, ++>For the algorithm iteration to the position of the jth locust in the d dimension at the t-th time, dij (t) is the distance between the ith locust and the jth locust of the locust population at the t-th iteration,/>Is the current optimal individual position,/->The optimal locust position after Cauchy mutation is that r is a random number on (0, 1), p is mutation probability, and p=0.7 is selected through experiments.
7. The ultra-short term wind power prediction method based on IGOA optimization ELM according to claim 1, wherein in the step 6, RMSE, MAE and E are as follows max The three evaluation index formulas are as follows:
E max ＝max(|P ti -P yi |) (14)
wherein: n is the number of samples, P ti For the actual power at time i, P yi For the predicted power at time i, P cap And starting up the total capacity for the wind farm.
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Application publication date: 20211001 Assignee: Shanghai Yanqiao Information Technology Co.,Ltd. Assignor: HUAIYIN INSTITUTE OF TECHNOLOGY Contract record no.: X2023980047724 Denomination of invention: A Ultra Short Term Wind Power Prediction Method Based on IGOA Optimized ELM Granted publication date: 20230822 License type: Common License Record date: 20231121 |