CN111461297A - Solar irradiation quantity optimization prediction algorithm based on MPC and E L M neural network - Google Patents

Solar irradiation quantity optimization prediction algorithm based on MPC and E L M neural network Download PDF

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CN111461297A
CN111461297A CN202010217224.3A CN202010217224A CN111461297A CN 111461297 A CN111461297 A CN 111461297A CN 202010217224 A CN202010217224 A CN 202010217224A CN 111461297 A CN111461297 A CN 111461297A
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郭苏
章晗
丁菲
王嘉乐
张睿
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Hohai University HHU
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Abstract

The invention discloses a solar irradiation amount optimization prediction algorithm based on MPC and an E L M neural network, which comprises the steps of 1, establishing an E L M neural network model based on MPC, 2, collecting historical data, 3, carrying out normalization processing, 4, training to obtain an optimal E L M neural network model, 5, setting rolling prediction parameters, 6, carrying out MPC rolling prediction, including 61, collecting t-M time historical data, 62, carrying out initial prediction, 63, collecting t time data, 64, calculating process prediction errors, 65, correcting the initial prediction data, 66, carrying out rolling prediction, determining input variables by selecting meteorological factors directly related to solar irradiation amount, such as ambient temperature, relative humidity and cloud amount, secondly, carrying out irradiation prediction by adopting the E L M neural network model, and finally, improving the precision of the solar irradiation amount prediction data by utilizing the rolling idea optimization of MPC.

Description

Solar irradiation quantity optimization prediction algorithm based on MPC and E L M neural network
Technical Field
The invention relates to a solar irradiation prediction algorithm, in particular to a solar irradiation optimization prediction algorithm based on an MPC (MPC) and an E L M neural network.
Background
With the gradual exhaustion of fossil energy and the increasing increase of ecological environment pollution, the exploration and development of renewable energy sources are reluctant to the world nowadays. Solar energy is one of the most major renewable energy sources worldwide, and photovoltaic power generation has become the major form of renewable energy power generation worldwide in recent years. Solar energy has intermittence, fluctuation and difficult predictability, and the instability of the photovoltaic power generation energy source causes the instability of the power generation power, thereby influencing the stability of the power grid power in China. Therefore, the large-scale photovoltaic power generation grid connection brings challenges to the dispatching management and the safe operation of the power grid system. An accurate solar irradiance information acquisition and prediction method is explored and found, and is the basis for accurately predicting photovoltaic power.
At present, a great deal of work is carried out on solar irradiation prediction at home and abroad, and common prediction methods can be classified into 2 types.
The first method comprises the following steps: based on detailed numerical weather forecast (NWP), the ultra-short-term solar irradiation amount is predicted by utilizing an observed numerical weather information and physical calculation model of irradiation amount, and the currently used prediction methods mainly comprise the following steps:
1. and (3) considering the matching degree of the load and the power supply time sequence, combining big data analysis, selecting an application market of the distributed photovoltaic power generation in the catering industry, and carrying out benefit analysis and sensitivity analysis.
2. The solar radiation dose is estimated by using a short-time prediction technology of albedo (albedo) and a satellite cloud picture.
3. The method comprises the steps of shooting a foundation cloud picture through a sky imager, fully acquiring cloud data, carrying out a series of image processing on the foundation cloud picture, and predicting solar irradiance after five minutes by matching with historical irradiance data.
4. The relation between the solar radiation intensity, the atmospheric temperature, the relative humidity, the wind condition, the weather type and the photovoltaic power generation power is visually analyzed through historical power generation data and meteorological data, and then correlation analysis is carried out on meteorological elements and photovoltaic output power by utilizing a correlation coefficient method.
5. And screening a data sequence with similar meteorological characteristics with the prediction time period from a large amount of data through a data mining technology, and predicting the photovoltaic power generation power by adopting a grey correlation degree theory.
However, although the first method has high prediction accuracy, it requires complicated satellite observation information and analysis methods, and is difficult to implement at present in China.
The second method comprises the following steps: through modeling historical data, a change rule of the irradiation amount is simulated, and then the future irradiation amount is predicted, and the specific prediction methods used mainly include the following methods:
1. artificial intelligence methods, such As Neural Networks (ANN), have been widely used in solar irradiance prediction. Although various environmental factors can be comprehensively considered, the method generally has the defects of low training speed, easy falling into local optimal solution and the like. For example, the BP neural network method has the defects that the optimal parameters of the network structure are difficult to determine, the convergence time is too long, and the method is easy to fall into the difficulty of local optimization.
2. Solar irradiance short-term prediction model in photovoltaic power generation based on Nash-Sut-Cliffe. The model firstly utilizes a Nash-Sut-Cliffe equation to establish a solar irradiance intensity model in photovoltaic power generation, determines the sunlight duration, radiation and minimum maximum temperature difference of the outdoor environment as main influence factors and conditions of a solar irradiance prediction model, and has low prediction precision.
3. L SSVM model, because the randomness of solar radiation is very large, the accuracy of a single least square support vector machine (L SSVM) model established by the traditional method is not high.
4. The E L M neural Network has the defects that an irradiance prediction model has an excessively high input spatial dimension, so that the structure of the irradiance prediction model is excessively complex, great difficulty is brought to learning and training, and meanwhile, the problem that meteorological factors and other information related to irradiance influence the prediction accuracy of the irradiance prediction model is solved.
Predictive control, or Model Predictive Control (MPC), is one of the only advanced control methods that have been successfully applied in industrial control, and is a control algorithm based on a predictive process model that determines future inputs and outputs from historical information of the process. Predictive control is an algorithm for optimal control, and calculates future control actions based on a compensation function or a performance function. The optimization process of the predictive control is not completed off-line once, and is repeatedly performed on-line within a limited moving time interval. The moving time interval is called a limited time domain, which is the biggest difference from the traditional optimal control, which uses a performance function to judge the global optimization. For a complex system with dynamic characteristic change and uncertain factors, the optimal performance does not need to be judged in a global scope, so the rolling optimization method is very suitable for the complex system. Predictive control is also an algorithm for feedback control. If the model and the process are matched wrongly or the control performance problem is caused by uncertain factors of the system, the predictive control can compensate errors or correct model parameters according to online identification, and the method has strong anti-jamming capability and robustness. Meanwhile, the MPC can conveniently take various constraint conditions into account, has no specific requirements on the form of a prediction model, and is suitable for the solar irradiation resource prediction problem containing various factors such as the randomness of solar irradiation resources, the intermittence and the volatility of photovoltaic power generation power and the like.
Therefore, if the MPC is combined with the E L M neural network, the model prediction error of the E L M neural network can be greatly reduced.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a solar irradiance optimization prediction algorithm based on MPC and E L M neural networks, the solar irradiance optimization prediction algorithm based on MPC and E L M neural networks firstly determines input variables by selecting meteorological factors directly related to solar irradiance, such as ambient temperature, relative humidity and cloud cover, secondly performs irradiance prediction by adopting an E L M neural network model, and finally improves the accuracy of solar irradiance prediction data by utilizing the rolling optimization idea of MPC.
In order to solve the technical problems, the invention adopts the technical scheme that:
an MPC and E L M neural network-based solar irradiance optimization prediction algorithm comprises the following steps:
the method comprises the following steps of 1, establishing an MPC-based E L M neural network model, wherein the established E L M neural network model comprises an input layer, a hidden layer and an output layer, the input layer comprises four input nodes of t-M moment historical irradiation dose, t-M moment environmental temperature, t-M moment relative humidity and t-M moment cloud dose, the output layer is provided with n output nodes, and the n output nodes are the irradiation dose at the future t moment, the irradiation dose at the future t + M moment and the irradiation dose … … at the future t + M (n-1) moment, wherein n is more than or equal to 1, and M is the prediction interval duration.
Step 2, collecting historical data: and selecting the solar irradiation amount of the previous five years and the corresponding environmental factor data as historical data samples. The environmental factor data includes, among other things, ambient temperature, relative humidity, and cloud cover.
Step 3, normalization treatment: the collected historical data samples are normalized and all mapped to the (0,1) range.
And 4, training, namely training the E L M neural network model established in the step 1 by using the normalized historical data sample in the step 3 to obtain an optimal connection weight and an optimal E L M neural network model with the optimal connection weight.
And 5, setting rolling prediction parameters: and determining a rolling prediction time period t to (t + N-1) according to the sunshine condition of a prediction place, wherein N is the number of prediction time points and is more than or equal to m (N-1). The prediction interval duration m is set according to the prediction purpose requirement.
Step 6, MPC rolling prediction, comprising the following steps:
step 61, collecting historical data at the t-m moment: and collecting historical irradiation amount at the t-m moment, ambient temperature at the t-m moment, relative humidity at the t-m moment and cloud cover at the t-m moment.
Step 62, initial prediction, namely inputting the collected t-M time historical data into an optimal E L M neural network model to obtain future t time exposure yr(t), future irradiation amount y at time t + mr(t + m) and the future irradiation amount y at t +2mr(t +2m), … …, future t + m (n-1) time exposure yr(t+m(n-1))。
Step 63, collecting data at time t: collecting t moment irradiation amount y*(t), ambient temperature at time t, relative humidity at time t and cloud cover at time t.
Step 64, calculating a process prediction error (error) (t), wherein the specific calculation formula is as follows:
error(t)＝y*(t)-yr(t)
step 65, correcting the initial prediction data: correcting the other initial prediction data except the irradiation amount at the future time t obtained in the step 62 to form corrected irradiation amount prediction data, wherein the specific correction result is as follows:
yrev(t+m)＝yr(t+m)+α·error(t)
yrev(t+2m)＝yr(t+2m)+α·error(t)
……
yrev(t+m(n-1))＝yr(t+m(n-1))+α·error(t)
wherein, yrev(t+m)、yrev(t+2m)、yrev(t + m (n-1)) the irradiation dose is corrected at a time t + m, a time t +2m and a time t + m (n-1) respectively in the future, and α is an error correction coefficient.
Step 66, rolling prediction: and (4) taking the data at the time t acquired in the step 63 as historical data, namely, taking t as t + m, and repeating the steps 62 to 65 to obtain corrected irradiation dose prediction data at n-1 future times. And the rest is repeated until the rolling prediction end time t is equal to t + N.
In step 3, the normalization processing formula is:
Figure BDA0002424786060000041
in the formula xi(t)、
Figure BDA0002424786060000042
Respectively representing the values, x, before and after the normalization of the data at time timin、ximaxRespectively representing the minimum and maximum values in the sample data.
Further comprising step 7, predictive assessment: the corrected irradiance prediction data formed in step 65 is evaluated using the mean absolute percent error MAPE and the root mean square error RMSE. The calculation formula of MAPE and RMSE is as follows:
Figure BDA0002424786060000043
Figure BDA0002424786060000044
in the formula, Y (t) and YmAnd (t) the measured values and the corrected irradiation dose prediction data are respectively, and N is the number of the prediction time points.
In step 2, the data of the previous 1 year in the historical data sample is used as a prediction set. And taking the data of the previous 2-5 years in the historical data sample as a training set and a testing set.
In step 5, the determined rolling prediction time period is 6:00-18:00, and the prediction interval duration m is 1h, that is, N is 13.
The method has the advantages that the solar irradiance prediction data is optimized by predicting the solar irradiance of a time sequence through the E L M neural network, continuously performing rolling prediction by using the predicted value of the latest time point and assisting feedback correction by using the rolling optimization idea of the MPC, and the algorithm improves the prediction precision by using the respective advantages of the MPC and the E L M, and has important significance for accurately predicting the photovoltaic power generation amount.
Drawings
FIG. 1 shows a flow chart of the solar irradiance optimization prediction algorithm based on MPC and E L M neural networks.
Figure 2 shows a comparison of the predicted results of the three models in this example at 3/11 of 2019.
Fig. 3 shows a comparison of the predicted results of the three models in this example at 3/20 of 2019.
Fig. 4 shows a comparison of the predicted results of the three models in this example at 31/3/2019.
FIG. 5 shows a comparison of the predicted results of the three models in this example at 8/4/2019.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
In the description of the present invention, it is to be understood that the terms "left side", "right side", "upper part", "lower part", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and that "first", "second", etc., do not represent an important degree of the component parts, and thus are not to be construed as limiting the present invention. The specific dimensions used in the present example are only for illustrating the technical solution and do not limit the scope of protection of the present invention.
As shown in FIG. 1, a solar irradiance optimization prediction algorithm based on MPC and E L M neural networks comprises the following steps.
Step 1, establishing an E L M neural network model based on MPC.
The established E L M neural network model comprises an input layer, a hidden layer and an output layer.
The input layer comprises four input nodes of historical irradiation at the t-m moment, ambient temperature at the t-m moment, relative humidity at the t-m moment and cloud cover at the t-m moment.
The output layer has n output nodes, which are respectively: the future t moment irradiation dose, the future t + m moment irradiation dose, the future t +2m moment irradiation dose … … future t + m (n-1) moment irradiation dose. Wherein n is more than or equal to 1, and m is the prediction interval duration.
Step 2, collecting historical data: and selecting the solar irradiation amount of the previous five years and the corresponding environmental factor data as historical data samples. The environmental factor data comprises meteorological factors such as environmental temperature, relative humidity and cloud cover which are directly related to the solar irradiation amount.
In addition, the data of the previous 1 year in the historical data sample is used as a prediction set. And taking the data of the previous 2-5 years in the historical data sample as a training set and a testing set.
In this example, the historical data samples are irradiation dose and environmental factor data from 2014 to 2019, and the irradiation dose samples are divided into a training set, a testing set and a prediction set. The data from 2014 to 2018 are training data and testing data, namely training sets and testing sets, and the data from 2019 are prediction data, namely prediction sets. And determining the optimal number of nodes of the hidden layer through a training set and a testing set, and determining the application of each node parameter to a prediction set.
Step 3, normalization treatment: the collected historical data samples are normalized and all mapped to the (0,1) range.
Different evaluation indexes often have different dimensions and dimension units, and the condition can influence the result of data analysis. And (3) carrying out normalization processing on the input attribute by using Matlab software, mapping the historical irradiation dose, the environmental temperature, the relative humidity and the cloud amount to a (0,1) range for processing, and enabling each index to be in the same order of magnitude, so that the operation is more convenient.
Normalization formula:
Figure BDA0002424786060000061
in the formula xi(t)、
Figure BDA0002424786060000062
Respectively representing the values, x, before and after the normalization of the data at time timin、ximaxRespectively representing the minimum and maximum values in the sample data.
And 4, training, namely training the E L M neural network model established in the step 1 by using the normalized historical data sample in the step 3 to obtain an optimal connection weight and an optimal E L M neural network model with the optimal connection weight.
And 5, setting rolling prediction parameters: and determining a rolling prediction time period t to (t + N-1) according to the sunshine condition of a prediction place, wherein N is the number of prediction time points and is more than or equal to m (N-1). The prediction interval duration m is set according to the prediction purpose requirement.
In the present embodiment, the determined rolling prediction period is preferably 6:00 to 18:00, and the prediction interval duration m is 1h, that is, N is 13.
Step 6, MPC rolling prediction, comprising the following steps.
Step 61, collecting historical data at the t-m moment: and collecting historical irradiation amount at the t-m moment, ambient temperature at the t-m moment, relative humidity at the t-m moment and cloud cover at the t-m moment.
In this embodiment, it is preferable to collect historical exposure dose at the time of 5:00, ambient temperature at the time of 5:00, relative humidity at the time of 5:00, and cloud amount at the time of 5: 00.
Step 62, initial prediction, namely inputting the collected t-M time historical data into an optimal E L M neural network modelIn the future, the irradiation amount y at the time t is obtainedr(t), future irradiation amount y at time t + mr(t + m) and the future irradiation amount y at t +2mr(t +2m), … …, future t + m (n-1) time exposure yr(t+m(n-1))。
In the embodiment, n is preferably 4, so that historical data at the time of 5:00 is collected and input into an optimal E L M neural network model to obtain the future irradiation amount y at the time of 6:00r(6:00) future irradiation dose y at 7:00 timer(7:00) future irradiation dose y at 8:00 timer(8:00) future 9:00 hours of irradiation dose yr(9:00)。
Step 63, collecting data at time t: collecting t moment irradiation amount y*(t), ambient temperature at time t, relative humidity at time t and cloud cover at time t.
In the embodiment, the irradiation amount y at the time of 6:00 is preferably collected*(6:00), ambient temperature at time 6:00, relative humidity at time 6:00, and cloud cover at time 6: 00.
Step 64, calculating a process prediction error (error) (t), wherein the specific calculation formula is as follows:
error(t)＝y*(t)-yr(t)
in this embodiment, taking 6:00 as an example, the following are:
error(6:00)＝y*(6:00)-yr(6:00)
step 65, correcting the initial prediction data: correcting the other initial prediction data except the irradiation amount at the future time t obtained in the step 62 to form corrected irradiation amount prediction data, wherein the specific correction result is as follows:
yrev(t+m)＝yr(t+m)+α·error(t)
yrev(t+2m)＝yr(t+2m)+α·error(t)
……
yrev(t+m(n-1))＝yr(t+m(n-1))+α·error(t)
wherein, yrev(t+m)、yrev(t+2m)、yrev(t + m (n-1)) correcting the irradiation dose at the time t + m, the irradiation dose at the time t +2m and the irradiation dose at the time t + m (n-1) in the future respectively αA positive coefficient, used to overcome the undetectable interference, is typically α ═ 1.
In this example, the initial prediction data at time 7:00, 8:00, and 9:00 are corrected with a time 6:00 error (6:00) as follows:
yrev(7:00)＝yr(7:00)+α·error(6:00)
yrev(8:00)＝yr(8:00)+α·error(6:00)
yrev(9:00)＝yr(9:00)+α·error(6:00)
step 66, rolling prediction: and (4) taking the data at the time t acquired in the step 63 as historical data, namely, taking t as t + m, and repeating the steps 62 to 65 to obtain corrected irradiation dose prediction data at n-1 future times. And the rest is repeated until the rolling prediction end time t is equal to t + N.
In this example, the initial prediction data at time 8:00, 9:00, and 10:00 is then corrected by the time error (7:00), and so on, to achieve a time-by-time rolling prediction until 13 cycles to 18:00 are completed.
Step 7, prediction and evaluation: the corrected irradiance prediction data formed in step 65 is evaluated using the mean absolute percent error MAPE and the root mean square error RMSE. The calculation formula of MAPE and RMSE is as follows:
Figure BDA0002424786060000081
Figure BDA0002424786060000082
in the formula, Y (t) and YmAnd (t) the measured values and the corrected irradiation dose prediction data are respectively, and N is the number of the prediction time points.
In the application, 3 models are adopted to predict the solar irradiation quantities of different types respectively, so that the model combining MPC and E L M has higher prediction precision by comparison, and the specific prediction effect analysis is as follows.
Model 1 is a conventional prediction model BP model.
Model 2 is the E L M model.
Model 3 is a model combining MPC and E L M of the present invention, historical irradiation data, ambient temperature, relative humidity, and cloud cover are used as the inputs of the model to make a time-by-time prediction of the solar irradiance on the predicted day.
Taking Nanjing as an example, researching and analyzing the solar irradiation amount from 2014 to 2018, selecting examples of 11 days in 3 months in 2019, 20 days in 3 months in 2019, 31 days in 3 months in 2019 and 8 days in 4 months in 2019 for prediction, wherein the prediction time interval is 1 hour, and selecting 06: 00-18: 13 whole time points between 00 for the weather variable and the irradiation amount as the predicted time of day. The prediction results are shown in fig. 2 to 5.
And (4) selecting the average absolute percent error (MAPE) and the Root Mean Square Error (RMSE) to evaluate the corrected prediction result, wherein the error analysis time period is also 6:00-18: 00.
The error analysis results are shown in table 1:
Figure BDA0002424786060000083
the test results show that the solar irradiance optimization prediction algorithm based on the MPC and the E L M neural network can remarkably improve the solar irradiance prediction precision and provide a basis for photovoltaic power generation interval prediction.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.

Claims (5)

1. A solar irradiation quantity optimization prediction algorithm based on MPC and E L M neural networks is characterized by comprising the following steps:
the method comprises the following steps of 1, establishing an E L M neural network model based on MPC, wherein the established E L M neural network model comprises an input layer, a hidden layer and an output layer, wherein the input layer comprises four input nodes of t-M moment historical irradiation dose, t-M moment environmental temperature, t-M moment relative humidity and t-M moment cloud dose, the output layer is provided with n output nodes, and the input nodes are respectively irradiation dose at the future t moment, future t + M moment irradiation dose and future t +2M moment irradiation dose … … and irradiation dose at the future t + M (n-1) moment, wherein n is more than or equal to 1, and M is prediction interval duration;
step 2, collecting historical data: selecting the solar irradiation amount of the previous five years and corresponding environmental factor data as historical data samples; wherein the environmental factor data comprises ambient temperature, relative humidity and cloud cover;
step 3, normalization treatment: normalizing the collected historical data samples, and mapping the samples to a (0,1) range;
step 4, training, namely training the E L M neural network model established in the step 1 by using the normalized historical data sample in the step 3 to obtain an optimal connection weight and an optimal E L M neural network model with the optimal connection weight;
and 5, setting rolling prediction parameters: determining a rolling prediction time period t to (t + N-1) according to the sunshine condition of a prediction place, wherein N is the number of prediction time points and is more than or equal to m (N-1); setting a prediction interval duration m according to the prediction purpose requirement;
step 6, MPC rolling prediction, comprising the following steps:
step 61, collecting historical data at the t-m moment: collecting historical irradiation amount at the t-m moment, ambient temperature at the t-m moment, relative humidity at the t-m moment and cloud cover at the t-m moment;
step 62, initial prediction, namely inputting the collected t-M time historical data into an optimal E L M neural network model to obtain future t time exposure yr(t), future irradiation amount y at time t + mr(t + m) and the future irradiation amount y at t +2mr(t +2m), … …, future t + m (n-1) time exposure yr(t+m(n-1))；
Step 63, collecting data at time t: collecting t moment irradiation amount y*(t), ambient temperature at time t, relative humidity at time t and cloud cover at time t;
step 64, calculating a process prediction error (error) (t), wherein the specific calculation formula is as follows:
error(t)＝y*(t)-yr(t)
step 65, correcting the initial prediction data: correcting the other initial prediction data except the irradiation amount at the future time t obtained in the step 62 to form corrected irradiation amount prediction data, wherein the specific correction result is as follows:
yrev(t+m)＝yr(t+m)+α·error(t)
yrev(t+2m)＝yr(t+2m)+α·error(t)
yrev(t+m(n-1))＝yr(t+m(n-1))+α·error(t)
wherein, yrev(t+m)、yrev(t+2m)、yrev(t + m (n-1)) is the corrected irradiation dose at the future time t + m, the corrected irradiation dose at the future time t +2m and the corrected irradiation dose at the future time t + m (n-1), α is an error correction coefficient;
step 66, rolling prediction: taking the data at the time t acquired in the step 63 as historical data, namely, enabling t to be t + m, and repeating the steps 62 to 65 to obtain corrected irradiation dose prediction data at n-1 future times; and the rest is repeated until the rolling prediction end time t is equal to t + N.
2. The solar irradiance optimizing prediction algorithm based on the MPC and the E L M neural network as claimed in claim 1, wherein in the step 3, the normalization process is formulated as:
Figure FDA0002424786050000021
in the formula xi(t)、
Figure FDA0002424786050000022
Respectively representing the values, x, before and after the normalization of the data at time timin、ximaxRespectively representing the minimum and maximum values in the sample data.
3. The solar irradiance prediction optimization algorithm based on the MPC and the E L M neural network as claimed in claim 1, further comprising a step 7 of estimating the corrected irradiance prediction data formed in the step 65 by using the mean absolute percent error MAPE and the root mean square error RMSE, wherein the calculation formulas of MAPE and RMSE are as follows:
Figure FDA0002424786050000023
Figure FDA0002424786050000024
in the formula, Y (t) and YmAnd (t) the measured values and the corrected irradiation dose prediction data are respectively, and N is the number of the prediction time points.
4. The solar irradiance prediction algorithm based on the MPC and the E L M neural network as claimed in claim 1, wherein in step 2, the data of the previous 1 year in the historical data sample is used as a prediction set, and the data of the previous 2-5 years in the historical data sample is used as a training set and a test set.
5. The solar irradiance prediction algorithm based on the MPC and the E L M neural network as claimed in claim 1, wherein in step 5, the determined rolling prediction time period is 6:00-18:00, and the prediction interval duration M is 1h, that is, N is 13.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112215478A (en) * 2020-09-27 2021-01-12 珠海博威电气股份有限公司 Power coordination control method and device for optical storage power station and storage medium
CN113780640A (en) * 2021-08-27 2021-12-10 河北工业大学 TCN-Attention-based solar radiation prediction method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108388956A (en) * 2018-01-18 2018-08-10 华北电力大学 Consider the photovoltaic power prediction technique of attenuation
CN109902874A (en) * 2019-02-28 2019-06-18 武汉大学 A kind of micro-capacitance sensor photovoltaic power generation short term prediction method based on deep learning

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108388956A (en) * 2018-01-18 2018-08-10 华北电力大学 Consider the photovoltaic power prediction technique of attenuation
CN109902874A (en) * 2019-02-28 2019-06-18 武汉大学 A kind of micro-capacitance sensor photovoltaic power generation short term prediction method based on deep learning

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
李荣等: "基于改进粒子群算法的光伏最大功率跟踪研究", 《电源技术》 *
杨杰等: "基于小波与最小资源分配网络的超短期风电功率预测研究", 《电力系统保护与控制》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112215478A (en) * 2020-09-27 2021-01-12 珠海博威电气股份有限公司 Power coordination control method and device for optical storage power station and storage medium
CN112215478B (en) * 2020-09-27 2024-05-28 珠海博威电气股份有限公司 Power coordination control method and device for optical storage station and storage medium
CN113780640A (en) * 2021-08-27 2021-12-10 河北工业大学 TCN-Attention-based solar radiation prediction method

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