CN111612648B - Training method and device for photovoltaic power generation prediction model and computer equipment - Google Patents
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Abstract
The application relates to a training method and device for a photovoltaic power generation prediction model, computer equipment and a storage medium. The method comprises the following steps: obtaining similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point; smoothing the similar solar photovoltaic data, and extracting photovoltaic data trend characteristics of the similar days; training an initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured. By adopting the method, the prediction accuracy of the output power of the photovoltaic array can be improved.
Description
Technical Field
The present application relates to the field of power technology, and in particular, to a training method, apparatus, computer device, and storage medium for determining a model of photovoltaic power.
Background
Photovoltaic power generation is an important component in renewable energy sources, and in recent years, the development of distributed photovoltaic power stations, particularly photovoltaic low-price internet surfing, is gradually realized, and the photovoltaic power generation has strong applicability.
And after the distributed photovoltaic power station is connected with the grid in a large scale, the safety and stability of the power grid are greatly affected. In order to reduce the impact of the distributed photovoltaic power station on the stability of the power grid, the power grid resources are required to be reasonably scheduled. Therefore, the accurate prediction of the output power of the photovoltaic array is realized, and the method plays an important role in power dispatching and evaluating the operation condition of the photovoltaic array.
In the prior art, it is common to predict photovoltaic array output power directly based on data provided by a weather forecast system. However, the data provided by the weather forecast system is often subject to some interference and errors due to the influence of many uncertainty factors, which makes the prior art have larger prediction errors when predicting the output power of the photovoltaic array.
Therefore, the current output power prediction method of the photovoltaic array has the problem of low accuracy.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a training method, apparatus, computer device, and storage medium for a photovoltaic power generation prediction model that can improve the accuracy of output power prediction of a photovoltaic array.
A method of training a photovoltaic power generation predictive model, the method comprising:
obtaining similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point;
smoothing the similar solar photovoltaic data, and extracting photovoltaic data trend characteristics of the similar days;
training an initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured.
In one embodiment, the smoothing the similar solar photovoltaic data to extract the photovoltaic data trend characteristic of the similar day includes:
obtaining a photovoltaic data observation time sequence of the similar day according to the similar solar photovoltaic data;
substituting the photovoltaic data observation time sequence into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day;
determining a photovoltaic data smoothing time sequence of the similar day according to the time sequence prediction equation; the photovoltaic data smoothing time series is used to characterize the photovoltaic data trend characteristics of the similar days.
In one embodiment, the photovoltaic data includes meteorological data and power data, and training the initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model includes:
obtaining a model training sample; the model training sample comprises a meteorological data smoothing time sequence of the similar day and a power data smoothing time sequence of the similar day;
training the initial photovoltaic power generation prediction model based on the meteorological data smoothing time sequence and the power data smoothing time sequence;
and when the trained initial photovoltaic power generation prediction model meets preset training conditions, obtaining the optimized photovoltaic power generation prediction model.
In one embodiment, the photovoltaic power generation prediction model includes a plurality of neuron nodes, and the training the initial photovoltaic power generation prediction model based on the meteorological data smoothing time series and the power data smoothing time series includes:
inputting the meteorological data smoothing time sequence into the initial photovoltaic power generation prediction model, and determining node prediction values corresponding to the neuron nodes;
Determining node expected values corresponding to the neuron nodes according to the power data smoothing time sequence based on an error back propagation algorithm;
and optimizing model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node.
In one embodiment, the optimizing the model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node includes:
acquiring a parameter population corresponding to model parameters of the initial photovoltaic power generation prediction model based on a genetic algorithm; the parameter population includes a plurality of parameter individuals; each of the parameter individuals corresponds to a set of model parameters;
determining the fitness corresponding to each parameter individual according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node;
performing genetic operator operation on each parameter individual according to the corresponding fitness of each parameter individual to obtain an optimized parameter individual;
Obtaining the trained initial photovoltaic power generation prediction model according to model parameters corresponding to the optimized parameter individuals;
retraining the trained initial photovoltaic power generation prediction model until the trained initial photovoltaic power generation prediction model meets the preset training conditions.
In one embodiment, the performing genetic operator operation on the parameter individuals according to the fitness corresponding to each parameter individual to obtain optimized parameter individuals includes:
selecting the parameter individuals according to the corresponding fitness of each parameter individual to obtain the eliminated parameter individuals;
according to the model parameters corresponding to the eliminated parameter individuals, performing cross operation on the eliminated parameter individuals to obtain crossed parameter individuals;
and carrying out mutation operation on the crossed parameter individuals according to the model parameters corresponding to the crossed parameter individuals to obtain the optimized parameter individuals.
In one embodiment, the method further comprises:
acquiring a difference value between a node predicted value corresponding to the neuron node and a node expected value corresponding to the neuron node;
And when the difference value is smaller than the preset difference threshold value, judging that the trained initial photovoltaic power generation prediction model meets the preset training condition.
A training device for a photovoltaic power generation predictive model, the device comprising:
the acquisition module is used for acquiring similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point;
the preprocessing module is used for carrying out smoothing processing on the similar solar photovoltaic data and extracting photovoltaic data trend characteristics of the similar days;
the training module is used for training the initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
According to the training method, the device, the computer equipment and the storage medium of the photovoltaic power generation prediction model, the photovoltaic data of the photovoltaic array on the similar day corresponding to the day to be tested are sampled at intervals to obtain the similar solar photovoltaic data, the interference data and the noise data in the similar solar photovoltaic data are filtered through smoothing processing of the similar solar photovoltaic data, so that the photovoltaic data trend characteristics of the similar day are accurately extracted, the photovoltaic data trend characteristics of the similar day are used for training the initial photovoltaic power generation prediction model, the photovoltaic power generation prediction result of the photovoltaic array on the day to be tested can be accurately output by the optimized photovoltaic power generation prediction model obtained through training, and the output power prediction precision of the photovoltaic array is improved.
Drawings
FIG. 1 is an application environment diagram of a training method of a photovoltaic power generation prediction model in one embodiment;
FIG. 2 is a flow chart of a method for training a photovoltaic power generation predictive model in one embodiment;
FIG. 3 is a flowchart of a training method of a photovoltaic power generation prediction model according to another embodiment;
FIG. 4 is a block diagram of a training device for a photovoltaic power generation predictive model in one embodiment;
FIG. 5 is a training flow diagram of a method for training a photovoltaic power generation predictive model in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The training method of the photovoltaic power generation prediction model can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. Wherein, the server 110 first obtains similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point. The server 110 then smoothes the similar solar data, extracting the photovoltaic data trend characteristics for the similar days. Finally, the server 110 trains the initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured. In practical applications, the server 110 may be implemented as a stand-alone server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a training method of a photovoltaic power generation prediction model is provided, and an example of application of the method to the terminal in fig. 1 is described, including the following steps:
step S210, obtaining similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling photovoltaic data of the photovoltaic array on a similar day corresponding to the day to be measured at intervals; similar solar photovoltaic data has corresponding sampling time points.
The similar solar photovoltaic data are obtained by sampling photovoltaic data of the photovoltaic array on a similar day corresponding to the day to be measured at intervals.
The photovoltaic array may be a connection of multiple photovoltaic modules.
The photovoltaic data may refer to meteorological data of similar days and power data of the photovoltaic array.
The meteorological data may refer to irradiance data and ambient temperature data, ambient humidity data, wind data, etc. of similar days. In practical application, irradiance data may refer to coplanar irradiation data measured by an irradiator having an installation angle identical to the inclination angle of the photovoltaic panel.
Of course, the photovoltaic data may also include current data and voltage data for the photovoltaic array.
In particular implementations, the server 110 obtains photovoltaic data on similar days at certain time intervals, and samples the photovoltaic data to obtain similar solar photovoltaic data.
Step S220, performing smoothing treatment on the similar solar photovoltaic data, and extracting photovoltaic data trend characteristics of similar days.
The photovoltaic data trend feature may be a feature corresponding to a trend of change in photovoltaic data.
In a specific implementation, after the server 110 obtains the similar solar photovoltaic data, the server 110 may perform smoothing processing on the similar solar photovoltaic data according to a preset data smoothing algorithm, and filter interference data and noise data in the similar solar photovoltaic data to obtain smoothed similar solar photovoltaic data. Finally, the server 110 determines the photovoltaic data trend characteristic corresponding to the similar day according to the smoothed similar photovoltaic data.
Step S230, training an initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on a day to be measured.
The photovoltaic power generation prediction result may refer to power generation of the photovoltaic array within a certain period of time of a day to be measured, or may refer to power generation of the photovoltaic array within a certain period of time of a day to be measured.
In a specific implementation, after extracting and determining the photovoltaic data trend feature corresponding to the similar day, the server 110 may train the initial photovoltaic power generation prediction model by using the photovoltaic data trend feature to obtain an optimized photovoltaic power generation prediction model for outputting the photovoltaic power generation prediction result of the photovoltaic array on the day to be measured.
In particular, the server 110 may characterize the smoothed similar solar volt data as photovoltaic data trend corresponding to similar days. The smoothed similar solar photovoltaic data may include smoothed similar daily meteorological data and smoothed similar daily power data, among others. Then, the server 110 may construct an initial photovoltaic power generation prediction model, take the smoothed similar solar-meteorological data as training sample features, take the smoothed similar solar-powered data as training sample labels, and train the initial photovoltaic power generation prediction model, so as to obtain an optimized photovoltaic power generation prediction model that can be used for outputting photovoltaic power generation prediction results of the photovoltaic array on the day to be tested.
In practical applications, the server 110 may input weather data of the day to be measured, such as coplanar irradiation data of the day to be measured, environmental temperature data of the day to be measured, etc., to an optimized photovoltaic power generation prediction model, and output, through the optimized photovoltaic power generation prediction model, power generation power of the day to be measured within a certain period of time of the day to be measured, that is, a short-term power prediction result.
According to the training method of the photovoltaic power generation prediction model, the photovoltaic data of the photovoltaic array on the similar day corresponding to the day to be tested are sampled at intervals to obtain the similar solar photovoltaic data, the interference data and the noise data in the similar solar photovoltaic data are filtered through smoothing processing of the similar solar photovoltaic data, so that the photovoltaic data trend characteristics of the similar day are accurately extracted, the initial photovoltaic power generation prediction model is trained by adopting the photovoltaic data trend characteristics of the similar day, the photovoltaic power generation prediction result of the photovoltaic array on the day to be tested can be accurately output by the photovoltaic power generation prediction model obtained through training, and the output power prediction accuracy of the photovoltaic array is improved.
In another embodiment, smoothing the similar solar photovoltaic data to extract photovoltaic data trend characteristics of similar days, comprising: obtaining photovoltaic data observation time sequences of similar days according to the similar photovoltaic data; substituting the photovoltaic data observation time sequence into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day; determining a photovoltaic data smoothing time sequence of similar days according to a time sequence prediction equation; the photovoltaic data smoothing time series is used to characterize photovoltaic data trend characteristics for similar days.
The weighted moving average model may refer to a mathematical model that performs a weighted moving average on data. In practice, the weighted moving average method may be an exponential smoothing method based on time series.
In particular implementations, the server 110 obtains a photovoltaic data observation time series for similar days based on similar solar photovoltaic data. Then, the server 110 substitutes the photovoltaic data observation time sequence into a preset weighted moving average model to implement moving smoothing processing on the photovoltaic data observation time sequence by using an exponential smoothing method, and determines a time sequence prediction equation of similar days.
Specifically, the server 110 may set the photovoltaic data observation time series to { y } t If the number of terms on the moving average is n, the predicted value of the t+1st phase can be expressed as:
wherein y is t As the t-th actual value, M t (1) Representing the t-th primary sliding average value;is the t+1st predicted value (t is more than or equal to n); n is { y } t Number of original data contained.
The server 110 may then determine the t-th secondary moving average M t (2) Wherein the t-th secondary moving average M t (2) Can be expressed as:
then, the server 110 sets a time series { y } t The } can be expressed as a linear model of time t, i.e
y t ＝a+bt+c
Wherein c is a random term, which can be omitted, the t-th primary sliding average value M t (1) Can be expressed as:
thus, the server 110 can obtain the t-th secondary moving average M t (2) Can be expressed as:
thus, the server 110 calculates:
wherein,and->It can be noted that:
in summary, the server 110 determines the time series prediction equation for the similar day as;
finally, the server 110 determines a photovoltaic data smoothing time sequence for representing the photovoltaic data trend characteristics of the similar days according to the time sequence prediction equation, so as to rapidly and accurately smooth the similar photovoltaic data, filter the interference data and the noise data in the similar photovoltaic data, and obtain the smoothed similar photovoltaic data.
In another embodiment, the photovoltaic data includes meteorological data and power data, and training the initial photovoltaic power generation prediction model according to photovoltaic data trend characteristics of similar days to obtain an optimized photovoltaic power generation prediction model includes: obtaining a model training sample; the model training sample comprises a meteorological data smoothing time sequence of similar days and a power data smoothing time sequence of similar days; training an initial photovoltaic power generation prediction model based on the meteorological data smoothing time sequence and the power data smoothing time sequence; and when the trained initial photovoltaic power generation prediction model meets preset training conditions, obtaining an optimized photovoltaic power generation prediction model.
The photovoltaic data includes meteorological data and power data. In practical applications, when the photovoltaic data includes meteorological data and power data, the photovoltaic data smoothing time series includes meteorological data smoothing time series and power data smoothing time series.
Wherein the meteorological data comprises coplanar irradiance data and ambient temperature data. In practice, when the meteorological data comprises coplanar irradiation data and ambient temperature data, the meteorological data smoothing time sequence comprises an irradiation data smoothing time sequence and a temperature data smoothing time sequence.
The model training sample comprises a meteorological data smoothing time sequence of similar days and a power data smoothing time sequence of similar days. It should be noted that, the meteorological data smoothing time sequence may be used as a sample feature of the model training sample; the power data smoothing time series may be used as a sample tag for the model training samples.
In a specific implementation, in the process of training the initial photovoltaic power generation prediction model by the server 110 according to the photovoltaic data trend characteristics of similar days to obtain the optimized photovoltaic power generation prediction model, the method specifically may include: the server 110 first obtains the weather data smoothing time series as sample features of the model training samples and the power data smoothing time series as sample tags of the model training samples. The server 110 will then train the initial photovoltaic power generation predictive model based on the meteorological data smoothing time series and the power data smoothing time series. Specifically, the server 110 may input the weather data smoothing time series to the initial photovoltaic power generation prediction model, and determine a photovoltaic power generation prediction result corresponding to the weather data smoothing time series by processing the initial photovoltaic power generation prediction model. Then, the server 110 determines a photovoltaic power generation desired result corresponding to the weather data smoothing time series from the power data smoothing time series. Finally, the server 110 optimizes model parameters of the initial photovoltaic power generation prediction model according to the difference between the photovoltaic power generation prediction result and the photovoltaic power generation expected result. In practical application, the server 110 determines a model loss of the initial photovoltaic power generation prediction model according to a difference between a photovoltaic power generation prediction result and a photovoltaic power generation expected result, and optimizes model parameters of the initial photovoltaic power generation prediction model based on a preset model optimization algorithm so as to train the initial photovoltaic power generation prediction model. When the server 110 judges that the trained initial photovoltaic power generation prediction model meets the preset training conditions, an optimized photovoltaic power generation prediction model is obtained. In practical application, the model optimization algorithm may be at least one of a gradient descent algorithm and a genetic algorithm.
According to the technical scheme, in the process of training an initial photovoltaic power generation prediction model according to the trend characteristics of photovoltaic data on similar days to obtain an optimized photovoltaic power generation prediction model, the initial photovoltaic power generation prediction model is trained by adopting a meteorological data smoothing time sequence and a power data smoothing time sequence on similar days, so that the photovoltaic power generation prediction result of the photovoltaic array on the day to be detected can be accurately output by the optimized photovoltaic power generation prediction model obtained through training, and the output power prediction precision of the photovoltaic array is improved.
In another embodiment, the photovoltaic power generation predictive model includes a plurality of neuron nodes, and training the initial photovoltaic power generation predictive model based on the meteorological data smoothing time series and the power data smoothing time series includes: inputting the meteorological data smoothing time sequence into an initial photovoltaic power generation prediction model, and determining node prediction values corresponding to each neuron node; based on an error back propagation algorithm, determining a node expected value corresponding to each neuron node according to the power data smoothing time sequence; and optimizing model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node.
The photovoltaic power generation prediction model comprises a plurality of neuron nodes.
In a specific implementation, the training of the initial photovoltaic power generation prediction model by the server 110 based on the meteorological data smoothing time sequence and the power data smoothing time sequence may specifically include: the server 110 may input the meteorological data smoothing time sequence to the initial photovoltaic power generation prediction model, and further obtain node prediction values corresponding to each neuron node. The server 110 then determines node expectation values corresponding to the respective neuron nodes from the power data smoothing time series based on an error back propagation algorithm.
Specifically, the server 110 may determine a loss of the photovoltaic power generation prediction model, that is, a prediction error, according to the predicted value of the photovoltaic power generation prediction model output layer and the power data smoothing time series. Then, the server 110 determines the node expectation value corresponding to each neuron node from the loss of the photovoltaic power generation prediction model based on the error back propagation algorithm (back propagation algorithm).
Finally, the server 110 optimizes model parameters of the initial photovoltaic power generation prediction model according to differences between the node predicted values corresponding to the neuron nodes and the node expected values corresponding to the neuron nodes. In practical applications, the server 110 may use a genetic algorithm to optimize model parameters of the initial photovoltaic power generation prediction model based on a difference between a node predicted value corresponding to a neuron node and a node expected value corresponding to the neuron node.
According to the technical scheme, in the process of training the initial photovoltaic power generation prediction model, the node prediction values corresponding to the neuron nodes and the node expected values corresponding to the neuron nodes are determined, and model parameters of the initial photovoltaic power generation prediction model are accurately optimized based on the difference between the node prediction values corresponding to the neuron nodes and the node expected values corresponding to the neuron nodes, so that the photovoltaic power generation prediction result of the photovoltaic array on the day to be tested can be accurately output by the optimized photovoltaic power generation prediction model obtained through training, and the output power prediction precision of the photovoltaic array is improved.
In another embodiment, optimizing model parameters of an initial photovoltaic power generation prediction model according to a difference between a node predicted value corresponding to a neuron node and a node expected value corresponding to the neuron node includes: acquiring a parameter population corresponding to model parameters of an initial photovoltaic power generation prediction model based on a genetic algorithm; the parameter population comprises a plurality of parameter individuals; each parameter individual corresponds to a set of model parameters; determining the fitness corresponding to each parameter individual according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node; according to the corresponding fitness of each parameter individual, genetic operator operation is carried out on the parameter individual to obtain an optimized parameter individual; obtaining a trained initial photovoltaic power generation prediction model according to model parameters corresponding to the optimized parameter individuals; retraining the trained initial photovoltaic power generation prediction model until the trained initial photovoltaic power generation prediction model meets preset training conditions.
Wherein the parameter population comprises a plurality of parameter individuals. Each parameter individual corresponds to a set of model parameters.
Wherein the genetic operator manipulation may include at least one of a selection manipulation, a crossover manipulation, and a mutation manipulation.
In a specific implementation, in the process of optimizing the model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node, the server 110 may specifically include: the server 110 obtains a parameter population corresponding to model parameters of the initial photovoltaic power generation predictive model based on a genetic algorithm. Then, the server 110 determines the fitness corresponding to each parameter individual according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node based on the preset fitness function F.
Wherein, the fitness function can distinguish the individual quality, is used for natural selection. The fitness function has a variety of choices, and generally the smaller the function value, the higher the fitness, and generally the better the individual.
In practical application, the fitness function F may be:
wherein k is a coefficient, which can be selected according to practical conditions, f i Prediction output for the ith node, o i For the desired output of the ith node, n is the number of output nodes.
After the server 110 determines the fitness corresponding to each parameter individual, the server 110 may perform genetic operator operations such as selection operation, crossover operation, mutation operation, etc. on the parameter individual according to the fitness corresponding to each parameter individual, to obtain an optimized parameter individual; then, the server 110 obtains a trained initial photovoltaic power generation prediction model according to the model parameters corresponding to the optimized parameter individuals. Specifically, the server 110 may decode the optimized parameter population formed by the optimized parameter individuals to obtain decoded model parameters. Then, the server 110 uses the decoded model parameters as model parameters corresponding to the trained initial photovoltaic power generation prediction model.
Finally, the server 110 retrains the trained initial photovoltaic power generation prediction model until the trained initial photovoltaic power generation prediction model meets the preset training conditions.
According to the technical scheme of the embodiment, a parameter population corresponding to the model parameters of the initial photovoltaic power generation prediction model is obtained based on a genetic algorithm, and the fitness corresponding to each parameter individual in the parameter population is determined according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node, so that genetic operation can be carried out on each parameter individual based on the fitness corresponding to each parameter individual, and further, the model parameters of the initial photovoltaic power generation prediction model are accurately and rapidly optimized.
In another embodiment, performing genetic operator operation on each parameter individual according to the fitness corresponding to each parameter individual to obtain an optimized parameter individual, including: selecting the parameter individuals according to the corresponding fitness of each parameter individual to obtain the eliminated parameter individuals; according to the model parameters corresponding to the eliminated parameter individuals, carrying out cross operation on the eliminated parameter individuals to obtain crossed parameter individuals; and carrying out mutation operation on the crossed parameter individuals according to the model parameters corresponding to the crossed parameter individuals to obtain optimized parameter individuals.
In a specific implementation, in the process that the server 110 performs genetic operator operation on the parameter individuals according to the fitness corresponding to each parameter individual to obtain the optimized parameter individuals, the server 110 may perform selection operation on the parameter individuals according to the fitness corresponding to each parameter individual to obtain the parameter individuals after elimination. Specifically, the selection operation is a process of optimizing from the old population. The probability that an individual parameter is selected is related to the fitness function, the higher the fitness function value, the greater the probability that it is selected. Probability p of the ith individual being selected i Can be expressed as:
wherein F is i And N is the number of parameter individuals, wherein the fitness value is the i parameter individual.
Then, the server 110 performs a crossover operation on the eliminated parameter individuals according to the model parameters corresponding to the eliminated parameter individuals, so as to obtain crossed parameter individuals. Specifically, the crossover operation is to randomly select two individuals from among the eliminated parameter individuals, inherit excellent characteristic inheritance to offspring through exchange combination, and generate excellent individuals, namely, the first m chromosomes a l ，a m Crossing at j bitsCan be expressed as:
wherein b is a random value of 0 to 1.
Finally, the server 110 performs a mutation operation on the intersected parameter individual according to the model parameters corresponding to the intersected parameter individual, so as to obtain an optimized parameter individual. Specifically, a preset variation formula may be adopted to perform variation operation on the crossed parameter individuals, so as to obtain optimized parameter individuals.
Wherein the j gene a of the i-th individual ij The variation formula is as follows:
f(g)＝r 2 (1-g/G max ) 2
wherein a is max ，a min A is respectively a ij R is a random number between 0 and 1; r is (r) 2 Is an arbitrary number, G is the number of iterations, G max Is the maximum number of evolutions;
according to the technical scheme of the embodiment, in the process of carrying out genetic operator operation on the parameter individuals according to the fitness corresponding to each parameter individual to obtain the optimized parameter individuals, through genetic operations such as selection operation, crossover operation, mutation operation and the like on the parameter individuals in sequence, the optimal model parameters of the photovoltaic power generation prediction model can be rapidly determined, so that training of the initial photovoltaic power generation prediction model can be rapidly completed.
In another embodiment, the method further comprises: acquiring a difference value between a node predicted value corresponding to a neuron node and a node expected value corresponding to the neuron node; when the difference value is smaller than a preset difference threshold value, judging that the trained initial photovoltaic power generation prediction model meets preset training conditions.
In a specific implementation, after the server 110 obtains the node predicted values corresponding to the plurality of neuron nodes in the photovoltaic power generation prediction model and the node expected values corresponding to the plurality of neuron nodes, the server 110 may obtain a difference value between the node predicted values corresponding to the neuron nodes and the node expected values corresponding to the neuron nodes. Then, the server 110 determines whether the difference value is smaller than a preset difference threshold; when the difference value is smaller than the preset difference threshold, the server 110 determines that the trained initial photovoltaic power generation prediction model meets the preset training condition.
According to the technical scheme, the difference value between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node is obtained, and the trained initial photovoltaic power generation prediction model is timely and accurately judged to meet the preset training condition through the difference value.
In another embodiment, as shown in fig. 3, a training method of a photovoltaic power generation prediction model is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes the following steps: step S310, obtaining similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point. Step S320, obtaining the photovoltaic data observation time sequence of the similar day according to the similar solar photovoltaic data. And S330, substituting the photovoltaic data observation time sequence into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day. Step S340, determining a photovoltaic data smoothing time sequence of the similar day according to the time sequence prediction equation; the photovoltaic data smoothing time sequence is used for representing the photovoltaic data trend characteristics of the similar days; the photovoltaic data includes meteorological data and power data. Step S350, obtaining a model training sample; the model training samples include a weather data smoothing time series for the similar day and a power data smoothing time series for the similar day. Step S360, training the initial photovoltaic power generation prediction model based on the meteorological data smoothing time sequence and the power data smoothing time sequence. Step S370, when the trained initial photovoltaic power generation prediction model meets preset training conditions, obtaining the optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured. For specific limitations of the above steps, reference may be made to the specific limitations of a method for training a photovoltaic power generation predictive model.
It should be understood that, although the steps in the flowcharts of fig. 2 and 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in fig. 2 and 3 may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the execution of the steps or stages is not necessarily sequential, but may be performed in rotation or alternatively with at least a portion of the steps or stages in other steps or steps.
In one embodiment, as shown in fig. 4, there is provided a training apparatus of a photovoltaic power generation prediction model, the apparatus comprising:
an acquisition module 410 for acquiring similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point;
The preprocessing module 420 is configured to perform smoothing on the similar solar photovoltaic data, and extract photovoltaic data trend characteristics of the similar days;
the training module 430 is configured to train the initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days, so as to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured.
In one embodiment, the preprocessing module 420 is specifically configured to obtain the photovoltaic data observation time sequence of the similar day according to the similar solar photovoltaic data; substituting the photovoltaic data observation time sequence into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day; determining a photovoltaic data smoothing time sequence of the similar day according to the time sequence prediction equation; the photovoltaic data smoothing time series is used to characterize the photovoltaic data trend characteristics of the similar days.
In one embodiment, the photovoltaic data includes meteorological data and power data, and the training module 430 is specifically configured to obtain a model training sample; the model training sample comprises a meteorological data smoothing time sequence of the similar day and a power data smoothing time sequence of the similar day; training the initial photovoltaic power generation prediction model based on the meteorological data smoothing time sequence and the power data smoothing time sequence; and when the trained initial photovoltaic power generation prediction model meets preset training conditions, obtaining the optimized photovoltaic power generation prediction model.
In one embodiment, the photovoltaic power generation prediction model includes a plurality of neuron nodes, and the training module 430 is specifically configured to input the meteorological data smoothing time sequence to the initial photovoltaic power generation prediction model, and determine a node prediction value corresponding to each neuron node; determining node expected values corresponding to the neuron nodes according to the power data smoothing time sequence based on an error back propagation algorithm; and optimizing model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node.
In one embodiment, the training module 430 is specifically configured to obtain, based on a genetic algorithm, a parameter population corresponding to model parameters of the initial photovoltaic power generation prediction model; the parameter population includes a plurality of parameter individuals; each of the parameter individuals corresponds to a set of model parameters; determining the fitness corresponding to each parameter individual according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node; performing genetic operator operation on each parameter individual according to the corresponding fitness of each parameter individual to obtain an optimized parameter individual; obtaining the trained initial photovoltaic power generation prediction model according to model parameters corresponding to the optimized parameter individuals; retraining the trained initial photovoltaic power generation prediction model until the trained initial photovoltaic power generation prediction model meets the preset training conditions.
In one embodiment, the training module 430 is specifically configured to perform a selection operation on the parameter individuals according to the fitness corresponding to each parameter individual, so as to obtain the parameter individuals after being eliminated; according to the model parameters corresponding to the eliminated parameter individuals, performing cross operation on the eliminated parameter individuals to obtain crossed parameter individuals; and carrying out mutation operation on the crossed parameter individuals according to the model parameters corresponding to the crossed parameter individuals to obtain the optimized parameter individuals.
In one embodiment, the training device of the photovoltaic power generation prediction model further includes:
the difference value acquisition module is used for acquiring a difference value between a node predicted value corresponding to the neuron node and a node expected value corresponding to the neuron node;
and the judging module is used for judging that the trained initial photovoltaic power generation prediction model meets the preset training condition when the difference value is smaller than the preset difference threshold value.
For specific limitations on the training device of the photovoltaic power generation prediction model, reference may be made to the above limitation on the training method of the photovoltaic power generation prediction model, and no further description is given here. All or part of each module in the training device of the photovoltaic power generation prediction model can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
For ease of understanding by those skilled in the art, FIG. 5 provides a training flow chart of a method of training a photovoltaic power generation predictive model; as shown in fig. 5, the server 110 obtains similar solar photovoltaic data entered by the user; the server 110 then smoothes the similar solar volt data, training the initial BP neural network. The initial BP neural network has initial weight and threshold. Specifically, during the training of the initial BP neural network, the server 110 may acquire a training test error during the training process, and then calculate the fitness of each parameter individual according to the test error; and performing selection operation, crossover operation and mutation operation on each parameter individual to obtain a new parameter population. Then, the server 110 adjusts the BP neural network based on the new parameter population until a preset termination condition is satisfied, specifically, the server 110 may decode the new parameter population to obtain an optimized model parameter of the BP neural network, thereby obtaining an optimized BP neural network, and further determining the short-term power of the photovoltaic array in the day to be measured.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing training data of the photovoltaic power generation prediction model. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of training a photovoltaic power generation predictive model.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of a method of training a photovoltaic power generation predictive model as described above. The step of a method for training a photovoltaic power generation prediction model may be the step of a method for training a photovoltaic power generation prediction model in the above-described respective embodiments.
In one embodiment, a computer readable storage medium is provided, storing a computer program, which when executed by a processor, causes the processor to perform the steps of a method of training a photovoltaic power generation predictive model as described above. The step of a method for training a photovoltaic power generation prediction model may be the step of a method for training a photovoltaic power generation prediction model in the above-described respective embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of training a photovoltaic power generation predictive model, the method comprising:
obtaining similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point;
smoothing the similar solar photovoltaic data, and extracting photovoltaic data trend characteristics of the similar days; substituting the photovoltaic data observation time sequence of the similar day into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day; determining a photovoltaic data smoothing time sequence of similar days according to a time sequence prediction equation so as to represent photovoltaic data trend characteristics of the similar days; the photovoltaic data observation time sequence is obtained according to the similar solar photovoltaic data; the time series prediction equation is expressed as:
Wherein,represents the predicted value of the time series in the t+tau period, n is the number of terms of moving average, M t (1) Represents the t-th primary sliding average value, M, of the photovoltaic data observation time sequence t (2) A t-th secondary sliding average value representing the photovoltaic data observation time sequence;
training an initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured.
2. The method of claim 1, wherein smoothing the similar solar volt data to extract the similar day trend feature comprises:
obtaining a photovoltaic data observation time sequence of the similar day according to the similar solar photovoltaic data;
substituting the photovoltaic data observation time sequence into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day;
determining a photovoltaic data smoothing time sequence of the similar day according to the time sequence prediction equation; the photovoltaic data smoothing time series is used to characterize the photovoltaic data trend characteristics of the similar days.
3. The method of claim 2, wherein the photovoltaic data comprises meteorological data and power data, and wherein training the initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain the optimized photovoltaic power generation prediction model comprises:
obtaining a model training sample; the model training sample comprises a meteorological data smoothing time sequence of the similar day and a power data smoothing time sequence of the similar day;
training the initial photovoltaic power generation prediction model based on the meteorological data smoothing time sequence and the power data smoothing time sequence;
and when the trained initial photovoltaic power generation prediction model meets preset training conditions, obtaining the optimized photovoltaic power generation prediction model.
4. The method of claim 3, wherein the photovoltaic power generation predictive model includes a plurality of neuron nodes, the training the initial photovoltaic power generation predictive model based on the meteorological data smoothing time series and the power data smoothing time series comprising:
inputting the meteorological data smoothing time sequence into the initial photovoltaic power generation prediction model, and determining node prediction values corresponding to the neuron nodes;
Determining node expected values corresponding to the neuron nodes according to the power data smoothing time sequence based on an error back propagation algorithm;
and optimizing model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node.
5. The method according to claim 4, wherein optimizing the model parameters of the initial photovoltaic power generation prediction model according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node comprises:
acquiring a parameter population corresponding to model parameters of the initial photovoltaic power generation prediction model based on a genetic algorithm; the parameter population includes a plurality of parameter individuals; each of the parameter individuals corresponds to a set of model parameters;
determining the fitness corresponding to each parameter individual according to the difference between the node predicted value corresponding to the neuron node and the node expected value corresponding to the neuron node;
performing genetic operator operation on each parameter individual according to the corresponding fitness of each parameter individual to obtain an optimized parameter individual;
Obtaining the trained initial photovoltaic power generation prediction model according to model parameters corresponding to the optimized parameter individuals;
retraining the trained initial photovoltaic power generation prediction model until the trained initial photovoltaic power generation prediction model meets the preset training conditions.
6. The method according to claim 5, wherein the performing genetic operator operation on the parameter individuals according to the fitness corresponding to each parameter individual to obtain optimized parameter individuals includes:
selecting the parameter individuals according to the corresponding fitness of each parameter individual to obtain the eliminated parameter individuals;
according to the model parameters corresponding to the eliminated parameter individuals, performing cross operation on the eliminated parameter individuals to obtain crossed parameter individuals;
and carrying out mutation operation on the crossed parameter individuals according to the model parameters corresponding to the crossed parameter individuals to obtain the optimized parameter individuals.
7. The method according to claim 4, wherein the method further comprises:
acquiring a difference value between a node predicted value corresponding to the neuron node and a node expected value corresponding to the neuron node;
And when the difference value is smaller than the preset difference threshold value, judging that the trained initial photovoltaic power generation prediction model meets the preset training condition.
8. A training device for a photovoltaic power generation predictive model, the device comprising:
the acquisition module is used for acquiring similar solar photovoltaic data; the similar solar photovoltaic data are obtained by sampling the photovoltaic data of the photovoltaic array at intervals on a similar day corresponding to the day to be measured; the similar solar volt data has a corresponding sampling time point;
the preprocessing module is used for carrying out smoothing processing on the similar solar photovoltaic data and extracting photovoltaic data trend characteristics of the similar days; substituting the photovoltaic data observation time sequence of the similar day into a preset weighted moving average model, and determining a time sequence prediction equation of the similar day; determining a photovoltaic data smoothing time sequence of similar days according to a time sequence prediction equation so as to represent photovoltaic data trend characteristics of the similar days; the photovoltaic data observation time sequence is obtained according to the similar solar photovoltaic data; the time series prediction equation is expressed as:
wherein,represents the predicted value of the time series in the t+tau period, n is the number of terms of moving average, M t (1) Represents the t-th primary sliding average value, M, of the photovoltaic data observation time sequence t (2) A t-th secondary sliding average value representing the photovoltaic data observation time sequence;
the training module is used for training the initial photovoltaic power generation prediction model according to the photovoltaic data trend characteristics of the similar days to obtain an optimized photovoltaic power generation prediction model; the optimized photovoltaic power generation prediction model is used for outputting a photovoltaic power generation prediction result of the photovoltaic array on the day to be measured.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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