CN111178593A - Photovoltaic system output power prediction method and device - Google Patents
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Abstract
The invention relates to a method and a device for predicting output power of a photovoltaic system, wherein the method comprises the following steps: acquiring an influence factor value of the output power of the photovoltaic system; predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system; the method and the device predict the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system, improve the prediction precision of the photovoltaic output power and provide more accurate data support for the operation and optimized scheduling of the photovoltaic power generation scale.
Description
Technical Field
The invention relates to the technical field of power prediction, in particular to a method and a device for predicting output power of a photovoltaic system.
Background
Photovoltaic power generation, as the most important way of utilizing solar energy, has become an emerging growing point for renewable energy power generation. Solar energy resources are abundant, theoretical reserves are large, and development and utilization of new energy and renewable energy become more and more focuses. Solar photovoltaic power generation is considered as a solar energy utilization mode which has the highest conversion efficiency and long service life and can provide a large amount of electric power. With the access of a large-scale photovoltaic power station to a power grid in recent years, the output power of photovoltaic power generation has randomness and volatility, so that the safety, stability and economic operation of the power grid are influenced. The method has the advantages that the output power of the photovoltaic power station is accurately predicted, important decision support can be provided for power dispatching, the coordination and cooperation of a conventional power supply and photovoltaic power generation can be arranged comprehensively, the operation cost of the power system is effectively reduced, photovoltaic resources are fully utilized, and greater economic benefits and social benefits are obtained.
The photovoltaic power generation process is influenced by solar irradiance, ambient temperature, the characteristics of the photovoltaic cell and the like, and various operation working conditions exist. Meteorological factors such as solar irradiance and ambient temperature have significant influence on photovoltaic power generation. Due to the fact that solar irradiance, ambient temperature and the like have statistical characteristics with obvious differences under different weather conditions or different seasons, the problems that prediction accuracy is not high, a model cannot adapt to changes of input parameters, prediction performance is not stable enough and the problems are prone to falling into a local mechanism and difficult in parameter optimization process when a single model is used for achieving output power prediction of a photovoltaic system generally exist.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to predict the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system, and improve the prediction precision of the photovoltaic output power.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a photovoltaic system output power prediction method, which is improved in that the method comprises the following steps:
acquiring an influence factor value of the output power of the photovoltaic system;
and predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system.
Preferably, the influencing factors include: solar irradiance, ambient temperature, relative humidity, wind speed, wind direction, and barometric pressure.
Preferably, the predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system includes:
respectively carrying out normalization processing on the influence factor values of the output power of the photovoltaic system;
taking the influence factor value of the photovoltaic system output power after normalization processing as the input layer data of a pre-established BP neural network prediction model to obtain the output power normalization value of the photovoltaic system output by the model;
and performing inverse normalization processing on the output power normalization value of the photovoltaic system to obtain the output power of the photovoltaic system.
Further, the method for obtaining the pre-established BP neural network prediction model includes:
acquiring a historical output power sample value of the photovoltaic system and an influence factor sample value thereof;
and taking the influencing factor sample value of the historical output power sample value of the photovoltaic system as an input layer training sample of the BP neural network model, taking the historical output power sample value of the photovoltaic system as an output layer training sample of the BP neural network model, training the BP neural network model, and obtaining the pre-established BP neural network prediction model.
Further, the obtaining of the historical output power sample value of the photovoltaic system and the influence factor sample value thereof includes:
acquiring historical output power and influence factor values of the photovoltaic system, and respectively carrying out normalization processing on the historical output power and the influence factor values of the photovoltaic system;
and taking the historical output power of the photovoltaic system subjected to normalization processing and the influence factor value thereof as the historical output power sample value of the photovoltaic system and the influence factor sample value thereof.
Based on the same inventive concept, the invention also provides a photovoltaic system output power prediction device, and the improvement is that the device comprises:
the acquisition unit is used for acquiring the influence factor value of the output power of the photovoltaic system;
and the prediction unit is used for predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system.
Further, the influencing factors include: solar irradiance, ambient temperature, relative humidity, wind speed, wind direction, and barometric pressure.
Further, the prediction unit is specifically configured to:
respectively carrying out normalization processing on the influence factor values of the output power of the photovoltaic system;
taking the influence factor value of the photovoltaic system output power after normalization processing as the input layer data of a pre-established BP neural network prediction model to obtain the output power normalization value of the photovoltaic system output by the model;
and performing inverse normalization processing on the output power normalization value of the photovoltaic system to obtain the output power of the photovoltaic system.
Further, the method for obtaining the pre-established BP neural network prediction model includes:
acquiring a historical output power sample value of the photovoltaic system and an influence factor sample value thereof;
and taking the influencing factor sample value of the historical output power sample value of the photovoltaic system as an input layer training sample of the BP neural network model, taking the historical output power sample value of the photovoltaic system as an output layer training sample of the BP neural network model, training the BP neural network model, and obtaining the pre-established BP neural network prediction model.
Further, the obtaining of the historical output power sample value of the photovoltaic system and the influence factor sample value thereof includes:
acquiring historical output power and influence factor values of the photovoltaic system, and respectively carrying out normalization processing on the historical output power and the influence factor values of the photovoltaic system;
and taking the historical output power of the photovoltaic system subjected to normalization processing and the influence factor value thereof as the historical output power sample value of the photovoltaic system and the influence factor sample value thereof.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a method and a device for predicting the output power of a photovoltaic system, which are used for obtaining the influence factor value of the output power of the photovoltaic system; predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system; the method and the device predict the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system, particularly predict the output power by utilizing a pre-established neural network model, and the BP neural network can adapt to the working condition change within a certain range and provide good modeling precision, thereby improving the prediction precision of the photovoltaic output power and providing more accurate data support for the operation of the photovoltaic power generation scale and the optimized scheduling.
Drawings
FIG. 1 is a flow chart of a photovoltaic system output power prediction method of the present invention;
fig. 2 is a schematic diagram of a photovoltaic system output power prediction device according to the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a photovoltaic system output power prediction method, as shown in fig. 1, the method comprises the following steps:
acquiring an influence factor value of the output power of the photovoltaic system;
and predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system.
In an embodiment of the present invention, the influencing factors include: solar irradiance, ambient temperature, relative humidity, wind speed, wind direction, and barometric pressure.
In an embodiment of the present invention, the predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system includes:
respectively carrying out normalization processing on the influence factor values of the output power of the photovoltaic system;
taking the influence factor value of the photovoltaic system output power after normalization processing as the input layer data of a pre-established BP neural network prediction model to obtain the output power normalization value of the photovoltaic system output by the model;
and performing inverse normalization processing on the output power normalization value of the photovoltaic system to obtain the output power of the photovoltaic system.
Further, the method for obtaining the pre-established BP neural network prediction model includes:
acquiring a historical output power sample value of the photovoltaic system and an influence factor sample value thereof;
and taking the influencing factor sample value of the historical output power sample value of the photovoltaic system as an input layer training sample of the BP neural network model, taking the historical output power sample value of the photovoltaic system as an output layer training sample of the BP neural network model, training the BP neural network model, and obtaining the pre-established BP neural network prediction model.
Further, the obtaining of the historical output power sample value of the photovoltaic system and the influence factor sample value thereof includes:
acquiring historical output power and influence factor values of the photovoltaic system, and respectively carrying out normalization processing on the historical output power and the influence factor values of the photovoltaic system;
and taking the historical output power of the photovoltaic system subjected to normalization processing and the influence factor value thereof as the historical output power sample value of the photovoltaic system and the influence factor sample value thereof.
In the embodiment of the invention, Sigmoid is selected as a neuron activation function, and a genetic algorithm is adopted to optimize the initial weight and the threshold of the BP neural network.
In order to make the prediction result more accurate and practical, the sample data of the training model can be selected from historical data of time to be predicted, for example, the output power in spring needs to be predicted, and the sample data is the historical output power in spring and the influence factor value thereof.
Based on the same inventive concept, the present invention further provides a photovoltaic system output power prediction apparatus, as shown in fig. 2, the apparatus includes:
the acquisition unit is used for acquiring the influence factor value of the output power of the photovoltaic system;
and the prediction unit is used for predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system.
Further, the influencing factors include: solar irradiance, ambient temperature, relative humidity, wind speed, wind direction, and barometric pressure.
Further, the prediction unit is specifically configured to:
respectively carrying out normalization processing on the influence factor values of the output power of the photovoltaic system;
taking the influence factor value of the photovoltaic system output power after normalization processing as the input layer data of a pre-established BP neural network prediction model to obtain the output power normalization value of the photovoltaic system output by the model;
and performing inverse normalization processing on the output power normalization value of the photovoltaic system to obtain the output power of the photovoltaic system.
Further, the method for obtaining the pre-established BP neural network prediction model includes:
acquiring a historical output power sample value of the photovoltaic system and an influence factor sample value thereof;
and taking the influencing factor sample value of the historical output power sample value of the photovoltaic system as an input layer training sample of the BP neural network model, taking the historical output power sample value of the photovoltaic system as an output layer training sample of the BP neural network model, training the BP neural network model, and obtaining the pre-established BP neural network prediction model.
Further, the obtaining of the historical output power sample value of the photovoltaic system and the influence factor sample value thereof includes:
acquiring historical output power and influence factor values of the photovoltaic system, and respectively carrying out normalization processing on the historical output power and the influence factor values of the photovoltaic system;
and taking the historical output power of the photovoltaic system subjected to normalization processing and the influence factor value thereof as the historical output power sample value of the photovoltaic system and the influence factor sample value thereof.
In summary, the method and the device for predicting the output power of the photovoltaic system provided by the invention obtain the influence factor value of the output power of the photovoltaic system; predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system; the method and the device predict the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system, particularly predict the output power by utilizing a pre-established neural network model, and the BP neural network can adapt to the working condition change within a certain range and provide good modeling precision, thereby improving the prediction precision of the photovoltaic output power and providing more accurate data support for the operation of the photovoltaic power generation scale and the optimized scheduling.
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 for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for predicting photovoltaic system output power, the method comprising:
acquiring an influence factor value of the output power of the photovoltaic system;
and predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system.
2. The method of claim 1, wherein the influencing factors comprise: solar irradiance, ambient temperature, relative humidity, wind speed, wind direction, and barometric pressure.
3. The method of claim 1, wherein predicting the output power of the photovoltaic system based on the values of the influencing factors for the output power of the photovoltaic system comprises:
respectively carrying out normalization processing on the influence factor values of the output power of the photovoltaic system;
taking the influence factor value of the photovoltaic system output power after normalization processing as the input layer data of a pre-established BP neural network prediction model to obtain the output power normalization value of the photovoltaic system output by the model;
and performing inverse normalization processing on the output power normalization value of the photovoltaic system to obtain the output power of the photovoltaic system.
4. The method of claim 3, wherein the method of obtaining the pre-established BP neural network prediction model comprises:
acquiring a historical output power sample value of the photovoltaic system and an influence factor sample value thereof;
and taking the influencing factor sample value of the historical output power sample value of the photovoltaic system as an input layer training sample of the BP neural network model, taking the historical output power sample value of the photovoltaic system as an output layer training sample of the BP neural network model, training the BP neural network model, and obtaining the pre-established BP neural network prediction model.
5. The method of claim 4, wherein the obtaining photovoltaic system historical output power sample values and their impact factor sample values comprises:
acquiring historical output power and influence factor values of the photovoltaic system, and respectively carrying out normalization processing on the historical output power and the influence factor values of the photovoltaic system;
and taking the historical output power of the photovoltaic system subjected to normalization processing and the influence factor value thereof as the historical output power sample value of the photovoltaic system and the influence factor sample value thereof.
6. A photovoltaic system output power prediction apparatus, the apparatus comprising:
the acquisition unit is used for acquiring the influence factor value of the output power of the photovoltaic system;
and the prediction unit is used for predicting the output power of the photovoltaic system based on the influence factor value of the output power of the photovoltaic system.
7. The apparatus of claim 6, wherein the influencing factors comprise: solar irradiance, ambient temperature, relative humidity, wind speed, wind direction, and barometric pressure.
8. The apparatus as claimed in claim 6, wherein said prediction unit is specifically configured to:
respectively carrying out normalization processing on the influence factor values of the output power of the photovoltaic system;
taking the influence factor value of the photovoltaic system output power after normalization processing as the input layer data of a pre-established BP neural network prediction model to obtain the output power normalization value of the photovoltaic system output by the model;
and performing inverse normalization processing on the output power normalization value of the photovoltaic system to obtain the output power of the photovoltaic system.
9. The apparatus of claim 8, wherein the method for obtaining the pre-established BP neural network prediction model comprises:
acquiring a historical output power sample value of the photovoltaic system and an influence factor sample value thereof;
and taking the influencing factor sample value of the historical output power sample value of the photovoltaic system as an input layer training sample of the BP neural network model, taking the historical output power sample value of the photovoltaic system as an output layer training sample of the BP neural network model, training the BP neural network model, and obtaining the pre-established BP neural network prediction model.
10. The apparatus of claim 9, wherein the obtaining photovoltaic system historical output power sample values and their impact factor sample values comprises:
acquiring historical output power and influence factor values of the photovoltaic system, and respectively carrying out normalization processing on the historical output power and the influence factor values of the photovoltaic system;
and taking the historical output power of the photovoltaic system subjected to normalization processing and the influence factor value thereof as the historical output power sample value of the photovoltaic system and the influence factor sample value thereof.
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