CN111242371A - Photovoltaic power generation short-term prediction correction method based on non-iterative multi-model - Google Patents

Photovoltaic power generation short-term prediction correction method based on non-iterative multi-model Download PDF

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CN111242371A
CN111242371A CN202010025659.8A CN202010025659A CN111242371A CN 111242371 A CN111242371 A CN 111242371A CN 202010025659 A CN202010025659 A CN 202010025659A CN 111242371 A CN111242371 A CN 111242371A
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朱红路
尹万思
韩雨彤
史淯城
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North China Electric Power University
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Abstract

The invention discloses a photovoltaic power generation short-term prediction correction method based on a non-iterative multi-model, belonging to the technical field of new energy. And then establishing a plurality of correction model correction prediction results through a non-iterative method. The method avoids the influence of accumulated errors, can reduce the prediction error by about 2-6 percent, effectively improves the prediction precision, and is beneficial to the dispatching of a power system and the safe and stable operation of a power grid.

Description

Photovoltaic power generation short-term prediction correction method based on non-iterative multi-model
Technical Field
The invention belongs to the technical field of new energy. In particular to a photovoltaic power generation short-term prediction correction method based on a non-iterative multi-model.
Background
In recent decades, due to the advantages of abundant solar energy resources, cleanness, no pollution and inexhaustibility, the photovoltaic power generation technology is rapidly developed, and the number of photovoltaic power stations is rapidly increased in the global scope. With access of large-scale photovoltaic power stations to a power system, characteristics of intermittency, random fluctuation and the like of photovoltaic power generation bring many difficulties and challenges to power dispatching work. Therefore, an accurate photovoltaic power prediction result has important significance for safe and stable operation of the power system under the condition of high-proportion photovoltaic access.
The short-term prediction method of the photovoltaic power generation can be divided into two main categories, namely ① direct prediction method (statistical method) for directly predicting the output power of the photovoltaic system and ② indirect prediction method (physical method) for predicting the solar radiation and obtaining the photoelectric output power according to the photoelectric conversion efficiency.
Both the direct prediction method and the indirect prediction method are applied in different occasions, but the two methods cannot avoid the problem of large prediction error. Due to the random fluctuation of the photovoltaic output, when the photovoltaic output fluctuates greatly, an accurate result cannot be obtained only by depending on weather forecast values and historical data. Therefore, in order to reduce the negative impact of the error on the grid system, the power prediction error needs to be corrected. The correction method can improve the prediction precision and reduce the influence of uncertainty, and is an effective means for improving the engineering practicability of the photovoltaic power prediction system.
Disclosure of Invention
The invention aims to provide a photovoltaic power generation short-term prediction correction method based on a non-iterative multi-model, which is characterized by comprising the following steps of:
s1, processing original data, namely dividing historical environment data and numerical weather forecast data according to seasons;
s2, dividing meteorological data and photovoltaic output data according to seasons in the step S1, and establishing a prediction model of each season through training input and output variables;
s3, predicting and obtaining a result, namely selecting a proper model from the prediction models of each season established in the step S2 to predict, and obtaining the result;
s4, selecting correction data; selecting the required predicted power and the measured power from the result obtained in the step S3;
s5, establishing a correction model, and establishing correction models at different moments in a non-iterative mode;
and S6, correcting results, and synthesizing the outputs of different models to obtain correction power.
The collected raw data in the step S1 includes historical power data of the photovoltaic power station and historical meteorological data; the historical power data of the photovoltaic power station comprises historical irradiance, ambient temperature, humidity and wind speed data corresponding to the photovoltaic power station, and the numerical weather forecast data comprises numerical weather forecast irradiance, ambient temperature, humidity and wind speed data of the location of the photovoltaic power station; and classifying the collected historical power data and historical meteorological data of the photovoltaic power station according to seasons.
In step S2, a seasonal prediction model is established according to the classified data, and the method specifically includes the following steps:
s21, training input variables and output variables in different seasons by using a support vector machine, a BP neural network and an extreme learning machine respectively, and establishing a mapping relation between the input variables and the output variables in each season;
s22, obtaining prediction models of the support vector machine, the BP neural network and the extreme learning machine in four seasons of spring, summer, autumn and winter respectively.
In step S3, the photovoltaic output power is predicted by a seasonal model, and the method specifically includes the following steps:
s31, judging the season of the prediction data, and selecting a proper seasonal model for prediction by using three algorithms;
and S32, obtaining a photovoltaic power prediction result.
In step S4, the training and prediction data of the correction part is selected, which specifically includes the following steps:
s41, selecting the predicted power and the actually measured power quantity during training of a correction part of models;
s42, selecting the predicted power quantity according to the requirement to correct; during correction, selecting predicted power x (t +1), x (t +2), … x (t + L) and actual power x ' (t-k +1), … x ' (t-1) and x ' (t) to input into a model, and obtaining correction power at a corresponding moment; wherein, (x (t +1), x (t +2), … x (t + L) are predicted power at t +1, t +2, … t + L time respectively, and x ' (t-k +1), … x ' (t-1), x ' (t) are actual power at t-k +1, … t-1, t time respectively.
In step S5, training input and output variables using an extreme learning machine includes the following steps:
s51, training model parameters by using the selected training data through an extreme learning machine, and establishing a mapping relation between an input variable and an output variable;
is provided with M arbitrary data (x)i,ti) Wherein x isi＝[xi1，xi2……xim]T∈Rm，ti＝[ti1，ti2……tin]T∈RnThen, the output of the feedforward neural network with N hidden node excitation functions as G can be expressed as:
Figure BDA0002362350580000031
in the formula: a isi＝[a1i,a2i,……ami]TWeight vector of i hidden node and input node βi＝[βi1,βi2,……βin]TThe weight vectors of the ith hidden node and the output node are obtained; biIs the offset of the ith hidden node; n is the number of hidden nodes; equation (1) can be abbreviated as:
Hβ＝T (2)
Figure BDA0002362350580000041
in the formula: h is a hidden layer output matrix, and the ith column of H corresponds to an input x1,x2,……xnAn ith hidden layer output vector; the output weight may be obtained by solving a least squares solution of the system of linear equations (3);
Figure BDA0002362350580000042
the least squares solution is:
β＝H+T (5)
middle H+Is the Moore-Penrose generalized inverse of the hidden layer output matrix H.
S52, establishing L models by using a non-iterative method through an extreme learning machine.
In step S6, the correcting the short-term prediction result specifically includes the following steps:
s61, inputting numerical weather forecast data into the seasonal model of S32 to obtain preliminary prediction power;
and S62, substituting the predicted power into the correction model in the step S52 to obtain the final photovoltaic correction power.
The photovoltaic power short-term prediction correction method has the advantages that: and the short-term prediction result of the power is corrected through the continuity between the output sequences, so that the prediction precision is improved. And effective information is provided for smooth control of photovoltaic output and maintenance and overhaul of components of the photovoltaic power station, including operation and real-time scheduling of the power system. The invention has the following characteristics:
1. the prediction method based on the seasonal model provides possibility for improving the adaptability of the model to the output under different seasonal conditions, and can obtain higher prediction accuracy.
2. And the continuity between photovoltaic output sequences is considered, and the actual value is introduced into the correction model, so that the prediction precision is improved to a great extent. Provides guarantee for dispatching and safe and stable operation of the power system
3. Compared with the conventional iterative method, the non-iterative method avoids the influence caused by accumulated errors and has higher prediction precision.
Drawings
FIG. 1 is a flow chart of short-term predictive correction of photovoltaic power generation;
FIG. 2 is a diagram of an ELM structure;
FIG. 3 is a non-iterative correction model;
FIG. 4 is a predicted power corrected using 6 actual values; wherein a is the relation between the actual power and the predicted power after the first 6 values are corrected; b is the error distribution condition after the first 6 values are corrected;
FIG. 5 shows predicted output power for different seasons; wherein, a spring, b summer, c autumn and d winter;
FIG. 6 shows an error distribution of different months; wherein, a, RMSE and b, MAE;
Detailed Description
The invention provides a photovoltaic power generation short-term prediction correction method based on a non-iterative multi-model. The photovoltaic power multi-model interval prediction method of the invention is further described below with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart illustrating short term photovoltaic power generation prediction correction; the photovoltaic power generation short-term prediction correction method based on the non-iterative multi-model comprises the following specific steps:
s1, collecting historical operation data, historical environment data and numerical weather forecast data of the photovoltaic power station.
The historical operation data comprises historical power data of the photovoltaic power station, the historical environment data comprises historical irradiance, environment temperature, humidity and wind speed data corresponding to the photovoltaic power station, and the numerical weather forecast data comprises numerical weather forecast irradiance, environment temperature, humidity and wind speed data of the location of the photovoltaic power station. Selecting the data of 2018 years of a new energy power system national key laboratory photovoltaic empirical test power station of North China Power university, wherein the power station consists of a photovoltaic power generation system with the capacity of 250Kw, a solar energy meteorological station, a photovoltaic power station monitoring system and a numerical weather forecasting system, and the data sampling time is 15 minutes. The method selects a 10KW photovoltaic array to analyze, and classifies photovoltaic historical data according to seasons.
S2, establishing a seasonal prediction model according to the classified data, and specifically comprising the following steps:
and S21, training input variables and output variables in different seasons by using a support vector machine, a BP neural network and an extreme learning machine respectively, and establishing a mapping relation between the input variables and the output variables in each season.
S22, obtaining prediction models of the support vector machine, the BP neural network and the extreme learning machine in four seasons of spring, summer, autumn and winter respectively.
S3, predicting the photovoltaic output power through a seasonal model, and specifically comprising the following steps:
and S31, judging the season of the prediction data, and selecting a proper model for prediction.
And S32, obtaining a power prediction result.
The analysis of the prediction result shows that the frequency of the positive error is higher than that of the negative error, which indicates that the probability of the occurrence of the case that the predicted value is larger than the measured value is higher.
S4, selecting training and prediction data of the correction part, and specifically comprising the following steps:
and S41, selecting the predicted power and the actually measured power quantity during training of the correction part model.
And S42, selecting the predicted power quantity according to the requirement to correct. During correction, the predicted power x (t +1), x (t +2), … x (t + L) and the actual power x ' (t-k +1), … x ' (t-1) and x ' (t) are selected and input into a model, and the corrected power at the corresponding moment is obtained.
S5 training input and output variables by using an extreme learning machine, which comprises the following steps:
and S51, training model parameters by using the selected training data through an extreme learning machine, and establishing a mapping relation between the input variable and the output variable.
The principle of the extreme learning machine is shown in FIG. 2
Is provided with M arbitrary data (x)i,ti) Wherein x isi＝[xi1，xi2……xim]T∈Rm，ti＝[ti1，ti2……tin]T∈RnThen, the output of the feedforward neural network with N hidden node excitation functions as G can be expressed as:
Figure BDA0002362350580000071
in the formula: a isi＝[a1i,a2i,……ami]TWeight vector of i hidden node and input node βi＝[βi1,βi2,……βin]TThe weight vectors of the ith hidden node and the output node are obtained; biIs the offset of the ith hidden node; n is the number of hidden nodes. Equation (3) can be abbreviated as:
Hβ＝T (2)
Figure BDA0002362350580000072
in the formula: h is a hidden layer output matrix, and the ith column of H corresponds to an input x1,x2,……xnThe ith hidden layer outputs a vector. The output weight may be obtained by solving a least squares solution of the system of linear equations (3);
Figure BDA0002362350580000073
the least squares solution is β ═ H+T (5)
In the formula H+Is the Moore-Penrose generalized inverse of the hidden layer output matrix H.
S52, establishing L models by using a non-iterative method through an extreme learning machine.
When short-term prediction is performed, a rolling iteration method is often adopted, namely, a value obtained by each prediction is used as an actual value at the current moment and is input into a model. However, due to accumulation of errors, the prediction error increases with the increase of the prediction time, resulting in an increase in deviation of the prediction result. In consideration of the limitation of the iterative method, the iterative method is abandoned, and a non-iterative mode is selected.
Assuming that the photovoltaic output power needs to be corrected at time t for L future times, the input-output variables are shown in fig. 3.
Figure BDA0002362350580000081
Actual power, model input value; x (t +1), x (t +2), x (t +3), … and x (t + L) are predicted powers, and the bold parts are input values of different models respectively; the corrected power portions x '(t +1), x' (t +2), x '(t +3), …, x' (t + L) are the output values of the different models. The input data of training is a predicted power value at a certain moment and k real values before the predicted value. When correcting, the input data are respectively the predicted values of x (t +1) to x (t + L)
Figure BDA0002362350580000082
To
Figure BDA0002362350580000083
The output data is the correction value.
S6, correcting the short-term prediction result, which specifically comprises the following steps:
s61, inputting numerical weather forecast data into the seasonal model of S32 to obtain preliminary prediction power;
and S62, substituting the predicted power into the correction model in the step S52 to obtain the final photovoltaic correction power.
RMSE and MAE are selected as error evaluation indexes, and are as follows:
Figure BDA0002362350580000084
Figure BDA0002362350580000085
in the formula: p is installed capacity; n is the number of samples; e.g. of the typetRepresenting an error.
As shown in table 1, RMSE and MAE are 4.4774 and 1.9372, respectively, when corrected with past values. As the number increases, both the RMSE and MAE values begin to decrease, with RMSE and MAE reaching a minimum at the sixth value. After reaching this minimum point, the RMSE and MAE remain substantially stable and increase even slightly. That is, the best effect is obtained when the number of past values is 6. Here we select the top 6 value corrections.
TABLE 1 error table corrected by past different values
Figure BDA0002362350580000091
Fig. 4 is a graph of actual power versus predicted power and corrected power after correction using the first 6 values. Wherein a is the relation between the actual power and the predicted power after the first 6 values are corrected; it can be seen that the deviation of the predicted power from the actual power is large and the distribution is obvious above the dotted line y, i.e. the probability that the predicted power is greater than the actual power is higher than the probability that the actual power is greater than the predicted power. However, the points of correcting power are positioned at two sides of the dotted line, the distribution is concentrated, the integral deviation is not very large, only the individual value of the pole deviates far from the dotted line, and the error is large. b is the corrected error distribution for the first 6 values. As can be seen from the figure, the value of the prediction error is large, and the distribution range is wide, and the prediction error has distribution in the range of [ -3000,4000 ]. The corrected error value has obvious large amplitude convergence, the error is greatly reduced, most of the error is in the range of [ -1000,1000], and a small part of the error exceeds the range. This shows that after correction, the error is reduced and the prediction accuracy is improved. Although non-iterative methods can reduce the accumulation of errors, the prediction accuracy also decreases with increasing time scale. Moreover, the predicted value is corrected by using the adjacent actual value, the close connection between the two values is required to be ensured, the continuity on a certain time sequence is ensured, and the higher the continuity is, the higher the precision is. Once the time scale that needs to be corrected is too long, the error becomes larger and larger. In table 2, the error of the first correction value is small, and as the time distance between the correction value and the actual value becomes larger, the RMSE increases almost 1% each time, and the MAE increases about 0.5% each time. In particular, the error of the seventh and eighth correction values is already greater than 10%, which is more than a factor of two greater than the first 4.3764%. Therefore, a suitable correction timescale needs to be selected. The time resolution of the photovoltaic data used this time was 15 minutes, and the four points were 1 hour. From the above table it can be found that it is relatively reasonable to correct 4 points, i.e. the time is one hour.
TABLE 2 error cases when correction values are different
Figure BDA0002362350580000101
And the table 3 shows the statistics of the prediction errors of the photovoltaic power station in 2017. RMSE of BP, SVM and ELM is about 10%, and MAE is about 5%. And the two errors of the correction model are 5.6795% and 2.5028% respectively, compared with other model errors, the errors are almost reduced by one time, and the accuracy of the correction model can be effectively improved.
TABLE 3 different model errors
Figure BDA0002362350580000111
As shown in fig. 5 a spring, b summer, c autumn, and d winter, relationships between predicted values, actual values, and corrected values for different seasons are presented. As can be seen from fig. 5, the predicted curves obtained by the three algorithms have larger deviation and are smoother than the actual output curve, and the fluctuation characteristics of the photovoltaic output cannot be well fitted. Compared with the prediction curve, the correction curve not only reduces the error, but also has higher fitting degree with the actual curve. Since the predicted value is corrected one hour at a time by a non-iterative method, although accumulation of errors due to iteration can be prevented, the accuracy also decreases with time. It can also be seen that the first value of each correction has a reduced error compared to the last value of the previous correction, and that the subsequent correction value has an increased error over time.
As shown in fig. 6 a, RMSE and b, MAE, the overall trend of the three verification algorithms is consistent, with only slight differences in the next few months. The correction algorithm is trending with other algorithms, but there are some differences in some places (e.g., months 2, months 7, etc.). The correction algorithm can reduce most errors by more than one time. In addition, when observing fig. a and b, both RMSE and MAE show a tendency of rising first in descending, and then rising and descending again. Furthermore, the maximum of the errors for the three validation algorithms occurs in July, while the maximum of the correction errors occurs in August. The minimum of the errors for the three validation algorithms occurs in december, while the minimum of the correction errors occurs in january. This means that the prediction error is not the smallest, and the correction error is necessarily the smallest, and the two are not in a definite relationship. Among the three algorithms, the BP prediction speed is high, the fluctuation at the later stage is small, and the BP prediction speed is relatively stable. The SVM has slow prediction speed and difficult parameter selection, but the error in the first two months is obviously lower than that in the other two methods. The ELM converges quickly, but the error is sometimes large.
The invention considers the seasonal distribution characteristic of errors, establishes a seasonal prediction model, introduces an actual value into a correction model, and corrects the prediction result. In addition, compared with an iterative method, the non-iterative method used by the invention avoids the influence caused by accumulated errors and has higher prediction precision.

Claims (7)

1. A photovoltaic power generation short-term prediction correction method based on a non-iterative multi-model is characterized by comprising the following steps:
s1, processing original data, namely dividing historical environment data and numerical weather forecast data according to seasons;
s2, dividing meteorological data and photovoltaic output data according to seasons in the step S1, and establishing a prediction model of each season through training input and output variables;
s3, predicting and obtaining a result, namely selecting a proper model from the prediction models of each season established in the step S2 to predict, and obtaining the result;
s4, selecting correction data; selecting the required predicted power and the measured power from the result obtained in the step S3;
s5, establishing a correction model, and establishing correction models at different moments in a non-iterative mode;
and S6, correcting results, and synthesizing the outputs of different models to obtain correction power.
2. The non-iterative multi-model-based photovoltaic power generation short-term prediction correction method according to claim 1, wherein the raw data collected in the step S1 includes historical power data of photovoltaic power station and historical numerical weather forecast data; the historical power data of the photovoltaic power station comprises historical irradiance, ambient temperature, humidity and wind speed data corresponding to the photovoltaic power station; the numerical weather forecast data comprises numerical weather forecast irradiance, ambient temperature, humidity and wind speed data of the location of the photovoltaic power station; and classifying the collected historical power data and historical meteorological data of the photovoltaic power station according to seasons.
3. The non-iterative multi-model-based photovoltaic power generation short-term prediction correction method according to claim 1, wherein in the step S2, a seasonal prediction model is established according to the classified data, and the method specifically comprises the following steps:
s21, training input variables and output variables in different seasons by using a support vector machine, a BP neural network and an extreme learning machine respectively, and establishing a mapping relation between the input variables and the output variables in each season;
s22, obtaining prediction models of the support vector machine, the BP neural network and the extreme learning machine in four seasons of spring, summer, autumn and winter respectively.
4. The non-iterative multi-model-based photovoltaic power generation short-term prediction correction method according to claim 1, wherein in the step S3, the photovoltaic output power is predicted through a seasonal model, and the method specifically comprises the following steps:
s31, judging the season of the prediction data, and selecting a proper seasonal model for prediction by using three algorithms;
and S32, obtaining a photovoltaic power prediction result.
5. The non-iterative multi-model-based photovoltaic power generation short-term prediction correction method according to claim 1, wherein in the step S4, the training and prediction data of the correction part are selected, and the method specifically includes the following steps:
s41, selecting the predicted power and the actually measured power quantity during training of a correction part of models;
s42, selecting the predicted power quantity according to the requirement to correct; during correction, selecting predicted power x (t +1), x (t +2), … x (t + L) and actual power x ' (t-k +1), … x ' (t-1) and x ' (t) to input into a model, and obtaining correction power at a corresponding moment; wherein, (x (t +1), x (t +2), … x (t + L) are predicted power at t +1, t +2, … t + L time respectively, and x ' (t-k +1), … x ' (t-1), x ' (t) are actual power at t-k +1, … t-1, t time respectively.
6. The non-iterative multi-model-based photovoltaic power generation short-term prediction correction method according to claim 1, wherein in the step S5, an extreme learning machine is used to train input and output variables, and the method specifically comprises the following steps:
s51, training model parameters by using the selected training data through an extreme learning machine, and establishing a mapping relation between an input variable and an output variable;
is provided with M arbitrary data (x)i,ti) Wherein x isi＝[xi1，xi2……xim]T∈Rm，ti＝[ti1，ti2……tin]T∈RnThen, the output of the feedforward neural network with N hidden node excitation functions as G is expressed as:
Figure FDA0002362350570000031
in the formula: a isi＝[a1i,a2i,……ami]TWeight vector of i hidden node and input node βi＝[βi1,βi2,……βin]TThe weight vectors of the ith hidden node and the output node are obtained; biIs the ith hidden layerBiasing of the node; n is the number of hidden nodes; equation (1) is abbreviated as:
Hβ＝T (2)
Figure FDA0002362350570000032
in the formula: h is a hidden layer output matrix, and the ith column of H corresponds to an input x1,x2,……xnAn ith hidden layer output vector; the output weight may be obtained by solving a least squares solution of the system of linear equations (3);
Figure FDA0002362350570000033
the least squares solution is:
β＝H+T (5)
in the formula H+Moore-Penrose generalized inverse of hidden layer output matrix H;
s52, establishing L models by using a non-iterative method through an extreme learning machine.
7. The non-iterative multi-model-based photovoltaic power generation short-term prediction correction method according to claim 1, wherein in the step S6, the correction of the short-term prediction result specifically includes the following steps:
s61, inputting numerical weather forecast data into the seasonal model of S32 to obtain preliminary prediction power;
and S62, substituting the predicted power into the correction model in the step S52 to obtain the final photovoltaic correction power.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111967652A (en) * 2020-07-22 2020-11-20 国网浙江省电力有限公司电力科学研究院 Double-layer cooperative real-time correction photovoltaic prediction method
CN112926653A (en) * 2021-02-25 2021-06-08 山东大学 Photovoltaic power short-term prediction method and system based on small-scale weather type correction

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103208037A (en) * 2013-04-26 2013-07-17 国电南瑞南京控制系统有限公司 Online correction based power prediction method applicable to new energy power station
CN103996073A (en) * 2014-05-06 2014-08-20 国家电网公司 Photometric-network real-time-correction self-learning ARMA model photovoltaic power prediction method
CN106446440A (en) * 2016-10-11 2017-02-22 天津大学 Short-term photovoltaic generation power prediction method based on online sequential extreme learning machine
WO2017035884A1 (en) * 2015-08-31 2017-03-09 中国科学院广州能源研究所 Output power classification prediction system suitable for full life cycle of photovoltaic system
CN109816095A (en) * 2019-01-14 2019-05-28 湖南大学 Based on the network flow prediction method for improving gating cycle neural network
CN109961190A (en) * 2019-03-29 2019-07-02 北京鑫泰绿能科技有限公司 A kind of photovoltaic plant power forecasting method a few days ago based on study day degree of fitting cluster
CN110598896A (en) * 2019-07-26 2019-12-20 陕西省水利电力勘测设计研究院 Photovoltaic power prediction method based on prediction error correction

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103208037A (en) * 2013-04-26 2013-07-17 国电南瑞南京控制系统有限公司 Online correction based power prediction method applicable to new energy power station
CN103996073A (en) * 2014-05-06 2014-08-20 国家电网公司 Photometric-network real-time-correction self-learning ARMA model photovoltaic power prediction method
WO2017035884A1 (en) * 2015-08-31 2017-03-09 中国科学院广州能源研究所 Output power classification prediction system suitable for full life cycle of photovoltaic system
CN106446440A (en) * 2016-10-11 2017-02-22 天津大学 Short-term photovoltaic generation power prediction method based on online sequential extreme learning machine
CN109816095A (en) * 2019-01-14 2019-05-28 湖南大学 Based on the network flow prediction method for improving gating cycle neural network
CN109961190A (en) * 2019-03-29 2019-07-02 北京鑫泰绿能科技有限公司 A kind of photovoltaic plant power forecasting method a few days ago based on study day degree of fitting cluster
CN110598896A (en) * 2019-07-26 2019-12-20 陕西省水利电力勘测设计研究院 Photovoltaic power prediction method based on prediction error correction

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
朱卫平;汪志成;袁晓冬;刘新宇;刘志;: "一种光伏功率预测的RBF神经网络校正模型" *
梁沛;卢继平;钟璐;张奋强;: "非迭代与分时段最优的风电功率短期预测" *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111967652A (en) * 2020-07-22 2020-11-20 国网浙江省电力有限公司电力科学研究院 Double-layer cooperative real-time correction photovoltaic prediction method
CN111967652B (en) * 2020-07-22 2023-10-24 国网浙江省电力有限公司电力科学研究院 Double-layer collaborative real-time correction photovoltaic prediction method
CN112926653A (en) * 2021-02-25 2021-06-08 山东大学 Photovoltaic power short-term prediction method and system based on small-scale weather type correction
CN112926653B (en) * 2021-02-25 2022-08-26 山东大学 Photovoltaic power short-term prediction method and system based on small-scale weather type correction

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