CN111832800A - Photovoltaic power station power prediction method and device - Google Patents
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Abstract
The invention provides a method and a device for predicting power of a photovoltaic power station, which are used for acquiring forecast meteorological data and forecast air quality data of the photovoltaic power station; inputting the forecast meteorological data and the forecast air quality data into a power prediction model for prediction to obtain the predicted power of the photovoltaic power station; the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station, and the power prediction precision of the photovoltaic power station is greatly improved by combining a mesoscale area mode and an air quality mode and based on a power prediction module; according to the method, historical meteorological data and forecast meteorological data are obtained based on the mesoscale regional mode, historical PM2.5 concentration and forecast PM2.5 concentration are obtained based on the regional air quality mode, namely the influences of total cloud and PM2.5 on the power of the photovoltaic power station are considered, the photovoltaic short-term power forecasting capability and the photovoltaic short-term power forecasting effect under the meteorological conditions of cloud, haze and the like are improved, and a reference basis is provided for the dispatching and running of a power grid.
Description
Technical Field
The invention relates to the technical field of new energy power generation power prediction, in particular to a photovoltaic power station power prediction method and device.
Background
In recent years, our country has been the fastest developing country of the global photovoltaic power generation industry at present, and has accelerated development and utilization of renewable energy and energy transformation on a large scale. In the field of power grid regulation and control, in order to reduce the influence of uncertainty of photovoltaic output on power grid dispatching operation, a photovoltaic power prediction technology becomes an important support technology. In practical application, the influence of complicated weather conditions such as cloud and haze on the power generation capacity of the photovoltaic power station is large, and meanwhile, higher requirements on photovoltaic power prediction are provided.
The photovoltaic output power is closely related to the meteorological conditions, the power curve is smooth in a clearance state, but factors such as movement and transient of cloud layers, aerosol particle concentration change and the like can cause attenuation of the photovoltaic output power to different degrees, and the influence is nonlinear. In the prior art, the photovoltaic power station power prediction generally takes the total irradiance, temperature, air pressure, wind speed and wind direction of numerical weather prediction as prediction factors, and takes the prediction factors as the input of a prediction model, so that the photovoltaic power station power prediction is finally realized, and the prediction precision is low.
Disclosure of Invention
In order to overcome the defect of low prediction precision in the prior art, the invention provides a photovoltaic power station power prediction method, which is characterized by comprising the following steps:
acquiring forecast meteorological data and forecast air quality data of a photovoltaic power station;
inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station;
the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station.
The construction of the power prediction model comprises the following steps:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station, and taking the historical meteorological data, the historical air quality data and the historical measured power data as training data;
and training the neural network by using the radial basis function as an activation function of the hidden layer based on the training data to obtain a power prediction model.
Acquiring historical meteorological data and forecast meteorological data of the photovoltaic power station based on a mesoscale regional mode;
the historical meteorological data and the forecast meteorological data comprise total irradiance, air temperature, wind speed, wind direction, air pressure and total cloud amount of the photovoltaic power station.
Obtaining historical air quality data and forecast air quality data of the photovoltaic power station based on the regional air quality mode;
the historical air quality data and the forecast air quality data each include PM2.5 concentration data.
The historical meteorological data, historical air quality data and historical measured power data that acquire photovoltaic power plant to as training data, include:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station;
and based on the time nodes, performing normalization processing on the acquired data to obtain training data.
The radial basis functions are determined as follows:
in the formula, ρ (x, c)i) Is a radial basis function, x is input training data, ciσ is the width parameter, which is the center position corresponding to the ith neuron of the hidden layer.
In another aspect, the present invention further provides a photovoltaic power plant power prediction apparatus, including:
the acquisition module is used for acquiring forecast meteorological data and forecast air quality data of the photovoltaic power station;
the prediction module is used for inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station;
the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station.
The apparatus further comprises a modeling module comprising:
the acquisition unit is used for acquiring historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station and taking the historical meteorological data, the historical air quality data and the historical measured power data as training data;
and the training unit is used for training the neural network by using the radial basis function as an activation function of the hidden layer based on the training data to obtain a power prediction model.
The acquisition module is specifically configured to:
and acquiring forecast meteorological data of the photovoltaic power station based on the mesoscale regional mode, and acquiring forecast air quality data of the photovoltaic power station based on the regional air quality mode.
The obtaining unit is specifically configured to:
acquiring historical meteorological data of the photovoltaic power station based on the mesoscale regional mode;
acquiring historical air quality data of the photovoltaic power station based on the regional air quality mode;
historical measured power data are obtained based on a photovoltaic power station monitoring system.
The historical meteorological data and the forecast meteorological data comprise total irradiance, air temperature, wind speed, wind direction, air pressure and total cloud cover of the photovoltaic power station; the historical air quality data and the forecast air quality data each include PM2.5 concentration data.
The obtaining unit is specifically configured to:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station;
and based on the time nodes, performing normalization processing on the acquired data to obtain training data.
The training unit determines the radial basis function according to the following equation:
in the formula, ρ (x, c)i) Is a radial basisNumber, x is input training data, ciσ is the width parameter, which is the center position corresponding to the ith neuron of the hidden layer.
The technical scheme provided by the invention has the following beneficial effects:
according to the photovoltaic power station power prediction method, the forecast meteorological data and the forecast air quality data of the photovoltaic power station are obtained; inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station; the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station, and the power prediction precision of the photovoltaic power station is greatly improved by combining a mesoscale area mode and an air quality mode and based on a power prediction module;
historical meteorological data and forecast meteorological data are obtained based on a mesoscale regional mode, and PM2.5 concentration is obtained based on a regional air quality mode, namely the influence of total cloud amount and PM2.5 on the power of a photovoltaic power station is considered, so that the photovoltaic short-term power prediction capability and the prediction effect under meteorological conditions of cloudy and haze are improved;
the method takes the total cloud amount and the PM2.5 concentration of the photovoltaic power station as radiation attenuation factors, and combines a radial basis function neural network to construct a direct mapping relation model of meteorological elements and photovoltaic active power, so as to provide a reference basis for the dispatching and operation of a power grid.
Drawings
FIG. 1 is a flow chart of a method for predicting power of a photovoltaic power plant in an embodiment of the invention;
FIG. 2 is a graph of a total cloud cover of a photovoltaic power plant obtained based on a mesoscale regional mode in an embodiment of the present invention;
FIG. 3 is a plot of PM2.5 concentration obtained based on a zone air quality mode in an embodiment of the present disclosure;
FIG. 4 is a comparative graph of photovoltaic power plant power prediction in an embodiment of the present invention;
fig. 5 is a structural diagram of a photovoltaic power plant power prediction device in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Example 1
The embodiment 1 of the invention provides a photovoltaic power station power prediction method, a specific flow chart is shown in fig. 1, and the specific process is as follows:
s101: acquiring forecast meteorological data and forecast air quality data of a photovoltaic power station;
s102: inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station;
the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station.
Constructing a power prediction model, comprising:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station, and taking the historical meteorological data, the historical air quality data and the historical measured power data as training data;
and training the neural network by using the radial basis function as an activation function of the hidden layer based on the training data to obtain a power prediction model. The neuron of the input layer adopts historical meteorological data and historical PM2.5 concentration of the photovoltaic power station, the neuron of the hidden layer adopts the distance (Euclidean distance) between an input vector and a central vector as an independent variable of a function, a radial basis function is used as an activation function, and the neuron of the output layer is historical power of the photovoltaic power station (obtained based on a monitoring system of the photovoltaic power station).
And the data time resolution of the weather data forecast by the historical weather data, the historical air quality data and the historical measured power data is not less than 15 min.
The historical meteorological data and forecast meteorological data of the photovoltaic power station are acquired based on a mesoscale regional mode (namely a WRF mode);
the historical meteorological data and the forecast meteorological data comprise total irradiance, air temperature, wind speed, wind direction, air pressure and total cloud cover of the photovoltaic power station.
Historical air quality data and forecast air quality data of the photovoltaic power station are acquired based on a regional air quality mode (namely, a WRF-CHEM mode which is obtained by online complete coupling of a meteorological mode WRF and a chemical mode);
the historical air quality data and the forecast air quality data each include PM2.5 concentration data.
Obtain photovoltaic power plant's historical meteorological data, historical air quality data and historical actual measurement power data to as training data, include:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station;
and based on the time nodes, performing normalization processing on the acquired data to obtain training data.
The activation function, i.e. the radial basis function, of the hidden layer is determined as follows:
in the formula, ρ (x, c)i) Is a radial basis function, x is input training data, ciThe central position corresponding to the ith neuron of the hidden layer is represented by sigma which is a width parameter, the width parameter influences the action range of the neuron on the input information, and the smaller the width parameter is, the smaller the influence range of the neuron is represented.
In embodiment 1 of the present invention, a 2-month 27-28-day total cloud cover curve of a certain 20MW photovoltaic power station obtained based on a mesoscale regional mode is shown in fig. 2, and a 2-month 27-28-day PM2.5 concentration curve of a certain 20MW photovoltaic power station obtained based on a regional air quality mode is shown in fig. 3, as can be seen from fig. 2, the total cloud cover of the sky between 27 days of 2 months and 28 days of 2 months is less, and the coverage of the sky cloud cover between 28 days of 2 months is higher; as can be seen from FIG. 3, the PM2.5 particle concentration is high at night and low during the daytime, and the PM2.5 concentration is obviously higher than 27 days during the 28 days in the 2 months.
The photovoltaic power station is predicted by adopting the power prediction model established in the embodiment 1 of the invention, and the prediction result is shown in fig. 4. In fig. 4, the method 1 is a photovoltaic power station power prediction result obtained without considering the radiation attenuation factor, the method 2 is a photovoltaic power station power prediction result obtained by considering the radiation attenuation factor, and the solid line is the actually measured photovoltaic power station power.
As can be seen from fig. 4, the power of the photovoltaic power station obtained by the method 2 is closer to the actually measured power of the photovoltaic power station, the predicted power of 28 days in 2 months is entirely lower than 27 days in 2 months, and the change of the predicted power conforms to the characteristics of high total cloud number and high PM2.5 concentration in 28 days in 2 months.
Example 2
Based on the same inventive concept, embodiment 2 of the present invention further provides a photovoltaic power plant power prediction apparatus, as shown in fig. 5, the following describes the functions of each component in detail:
the acquisition module is used for acquiring forecast meteorological data and forecast air quality data of the photovoltaic power station;
and the prediction module is used for inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station.
The power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station.
The photovoltaic power plant power prediction device provided by embodiment 2 of the present invention further includes a modeling module, where the modeling module includes:
the acquisition unit is used for acquiring historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station and taking the historical meteorological data, the historical air quality data and the historical measured power data as training data;
and the training unit is used for training the neural network by using the radial basis function as an activation function of the hidden layer based on the training data to obtain a power prediction model.
The acquisition module is specifically configured to:
and acquiring forecast meteorological data of the photovoltaic power station based on the mesoscale regional mode, and acquiring forecast air quality data of the photovoltaic power station based on the regional air quality mode.
The obtaining unit is specifically configured to:
acquiring historical meteorological data of the photovoltaic power station based on the mesoscale regional mode;
acquiring historical air quality data of the photovoltaic power station based on the regional air quality mode;
historical measured power data are obtained based on a photovoltaic power station monitoring system.
The historical meteorological data and the forecast meteorological data comprise total irradiance, air temperature, wind speed, wind direction, air pressure and total cloud cover of the photovoltaic power station.
The historical air quality data and the forecast air quality data each include PM2.5 concentration data.
The obtaining unit is specifically configured to:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station;
and based on the time nodes, performing normalization processing on the acquired data to obtain training data.
The training unit determines the radial basis functions as follows:
in the formula, ρ (x, c)i) Is a radial basis function, x is input training data, ciσ is the width parameter, which is the center position corresponding to the ith neuron of the hidden layer.
For convenience of description, each part of the above-described apparatus is separately described as being functionally divided into various modules or units. Of course, the functionality of the various modules or units may be implemented in the same one or more pieces of software or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person of ordinary skill in the art can make modifications or equivalent substitutions to the specific embodiments of the present invention with reference to the above embodiments, and any modifications or equivalent substitutions which do not depart from the spirit and scope of the present invention are within the protection scope of the present invention as claimed in the appended claims.
Claims (13)
1. A photovoltaic power station power prediction method is characterized by comprising the following steps:
acquiring forecast meteorological data and forecast air quality data of a photovoltaic power station;
inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station;
the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station.
2. The photovoltaic power plant power prediction method of claim 1 wherein the building of the power prediction model comprises:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station, and taking the historical meteorological data, the historical air quality data and the historical measured power data as training data;
and training the neural network by using the radial basis function as an activation function of the hidden layer based on the training data to obtain a power prediction model.
3. The method of claim 2 in which the obtaining of historical and forecast meteorological data for the photovoltaic plant is based on a mesoscale regional mode;
the historical meteorological data and the forecast meteorological data comprise total irradiance, air temperature, wind speed, wind direction, air pressure and total cloud amount of the photovoltaic power station.
4. The method of claim 3 in which the obtaining of historical air quality data and forecast air quality data for the photovoltaic power plant is based on a regional air quality model;
the historical air quality data and the forecast air quality data each include PM2.5 concentration data.
5. The photovoltaic power plant power prediction method of claim 2 wherein the obtaining historical meteorological data, historical air quality data, and historical measured power data for a photovoltaic power plant as training data comprises:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station;
and based on the time nodes, performing normalization processing on the acquired data to obtain training data.
6. The photovoltaic power plant power prediction method of claim 2 wherein the radial basis functions are determined according to the following equation:
in the formula, ρ (x, c)i) Is a radial basis function, x is input training data, ciσ is the width parameter, which is the center position corresponding to the ith neuron of the hidden layer.
7. A photovoltaic power plant power prediction apparatus, comprising:
the acquisition module is used for acquiring forecast meteorological data and forecast air quality data of the photovoltaic power station;
the prediction module is used for inputting the forecast meteorological data and the forecast air quality data into a pre-constructed power prediction model for prediction to obtain the predicted power of the photovoltaic power station;
the power prediction model is obtained by training through a neural network based on historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station.
8. The photovoltaic power plant power prediction device of claim 7 characterized in that the device further comprises a modeling module comprising:
the acquisition unit is used for acquiring historical meteorological data, historical air quality data and historical measured power data of the photovoltaic power station and taking the historical meteorological data, the historical air quality data and the historical measured power data as training data;
and the training unit is used for training the neural network by using the radial basis function as an activation function of the hidden layer based on the training data to obtain a power prediction model.
9. The photovoltaic power plant power prediction device of claim 7 wherein the acquisition module is specifically configured to:
and acquiring forecast meteorological data of the photovoltaic power station based on the mesoscale regional mode, and acquiring forecast air quality data of the photovoltaic power station based on the regional air quality mode.
10. The photovoltaic power plant power prediction device of claim 9 wherein the obtaining unit is specifically configured to:
acquiring historical meteorological data of the photovoltaic power station based on the mesoscale regional mode;
acquiring historical air quality data of the photovoltaic power station based on the regional air quality mode;
historical measured power data are obtained based on a photovoltaic power station monitoring system.
11. The photovoltaic power plant power prediction device of claim 10 wherein the historical and forecasted meteorological data each include total irradiance, air temperature, wind speed, wind direction, air pressure, and total cloud for the photovoltaic power plant;
the historical air quality data and the forecast air quality data each include PM2.5 concentration data.
12. The photovoltaic power plant power prediction device of claim 8 wherein the obtaining unit is specifically configured to:
acquiring historical meteorological data, historical air quality data and historical measured power data of a photovoltaic power station;
and based on the time nodes, performing normalization processing on the acquired data to obtain training data.
13. The photovoltaic power plant power prediction device of claim 8 wherein the training unit determines the radial basis function as follows:
in the formula, ρ (x, c)i) Is a radial basis function, x is input training data, ciσ is the width parameter, which is the center position corresponding to the ith neuron of the hidden layer.
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| CN112132364A (en) * | 2020-11-02 | 2020-12-25 | 西安热工研究院有限公司 | Photovoltaic power station power prediction method, medium and equipment influenced by cloud layer |
| CN112446554A (en) * | 2020-12-18 | 2021-03-05 | 阳光电源股份有限公司 | Power prediction model establishing method, power prediction method and device |
| CN113095562A (en) * | 2021-04-07 | 2021-07-09 | 安徽天能清洁能源科技有限公司 | Ultra-short term power generation prediction method and device based on Kalman filtering and LSTM |
| CN116128170A (en) * | 2023-04-19 | 2023-05-16 | 深圳市峰和数智科技有限公司 | Photovoltaic power station power ultra-short-term prediction method and device and related equipment |
| CN117639662A (en) * | 2023-12-07 | 2024-03-01 | 电暴猿(上海)科技有限公司 | Intelligent monitoring-based photovoltaic power station power prediction method and system |
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2020
- 2020-05-28 CN CN202010465388.8A patent/CN111832800A/en active Pending
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| CN112132364A (en) * | 2020-11-02 | 2020-12-25 | 西安热工研究院有限公司 | Photovoltaic power station power prediction method, medium and equipment influenced by cloud layer |
| CN112132364B (en) * | 2020-11-02 | 2023-02-21 | 西安热工研究院有限公司 | Photovoltaic power station power prediction method, medium and equipment influenced by cloud layer |
| CN112446554A (en) * | 2020-12-18 | 2021-03-05 | 阳光电源股份有限公司 | Power prediction model establishing method, power prediction method and device |
| CN112446554B (en) * | 2020-12-18 | 2024-05-14 | 阳光电源股份有限公司 | Power prediction model establishment method, power prediction method and device |
| CN113095562A (en) * | 2021-04-07 | 2021-07-09 | 安徽天能清洁能源科技有限公司 | Ultra-short term power generation prediction method and device based on Kalman filtering and LSTM |
| CN116128170A (en) * | 2023-04-19 | 2023-05-16 | 深圳市峰和数智科技有限公司 | Photovoltaic power station power ultra-short-term prediction method and device and related equipment |
| CN117639662A (en) * | 2023-12-07 | 2024-03-01 | 电暴猿(上海)科技有限公司 | Intelligent monitoring-based photovoltaic power station power prediction method and system |
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