CN104268638A - Photovoltaic power generation system power predicting method of elman-based neural network - Google Patents
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Abstract
The invention discloses a photovoltaic power generation system power predicting method of an elman-based neural network. The method comprises the steps of obtaining generated power historical data and corresponding historical weather parameter information of photovoltaic power generation equipment at the related area, determining input data and output data of the neural network, determining the optimal number of hidden layer neurons, building the elman-based neural network accordingly, carrying out normalization processing on the generated power historical data and the historical weather parameter information, training the built neural network according to the data obtained after normalization processing is carried out, controlling prediction errors of the elman-based neural network to be within the preset range accordingly, regarding generated power historical data of one week before the prediction day and weather parameter data of the prediction day as input, and predicting the generated power of the prediction day through the trained neural network. The method has the advantages of being stable and high in time-varying adaptability and prediction precision, and can be widely applied to the field of photovoltaic power generation.
Description
Technical Field
The invention relates to the field of photovoltaic power generation, in particular to a photovoltaic power generation system power prediction method based on an elman neural network.
Background
Renewable energy power generation is a relatively efficient and clean renewable energy utilization mode, and is one of the most mature, large-scale development conditions and commercial development prospects in the current renewable energy utilization technology. Photovoltaic power generation is a main utilization mode of renewable energy sources and is a main component of a smart grid. The prediction of the short-term generated power is the key for successfully popularizing photovoltaic power generation, is the basis for the power dispatching department to make a power dispatching plan, and is an important guarantee for the benefit of self-built photovoltaic power generation systems of families or enterprises and the like.
However, all short-term solar photovoltaic power generation prediction methods are based on the same idea: firstly, establishing a prediction formula or model by using mathematical and physical theories and related data, and predicting the power generation capacity of the photovoltaic power station by using the prediction formula or model. According to the adopted mathematical physics theory and the prediction output quantity thereof, the photovoltaic power generation prediction method can be divided into two main categories: (1) a direct prediction method (also called a statistical method) for directly predicting the output power of the photoelectric system; (2) firstly, the solar radiation is predicted, and then an indirect prediction method (also called a physical method) for obtaining the photoelectric output power according to the photoelectric conversion efficiency is obtained.
The prediction method based on the statistical method comprises methods such as a probability method, a time series method, an artificial intelligence method and the like, and has the advantages of simple and clear program and no requirements on the position of a photovoltaic power station and power conversion parameters; the method has the disadvantages that environmental factors influencing photovoltaic power generation are not considered, a large amount of historical operating data of the photovoltaic power station is needed to ensure the accuracy of the forecasting result, and the fluctuation of the forecasting accuracy is easy to be overlarge due to the change of the environment. The prediction method based on the physical method is mainly based on the physical power generation principle of the photovoltaic power generation system. The method has the advantages that historical operation data is not needed, and the photovoltaic power station can be directly predicted after being built; the defect is that data such as a detailed topographic map of the photovoltaic power station, coordinates of the power station, a power curve of the photovoltaic power station and other related photoelectric conversion parameters are required.
Currently, the prediction method based on the BP neural network (one of artificial intelligence methods) is widely applied in the industry, but the prediction method based on the BP neural network still has the following defects:
(1) only feedforward is carried out without feedback, the sensitivity to historical data is too poor, and the memorized information of the learning mode is easy to disappear and is not stable enough;
(2) the capability of processing dynamic information is too weak, the characteristics of a photovoltaic power generation system in a dynamic process cannot be directly and dynamically reflected, the capability of adapting to time-varying characteristics is not provided, and the fluctuation of prediction precision is large.
Disclosure of Invention
In order to solve the technical problems, the invention aims to: the photovoltaic power generation system power prediction method based on the elman neural network is stable, has the capability of adapting to time-varying characteristics and is high in prediction accuracy.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a photovoltaic power generation system power prediction method based on an elman neural network comprises the following steps:
A. acquiring historical generated power data and corresponding historical weather parameter information of photovoltaic power generation equipment in relevant areas;
B. determining input and output data of a neural network, and determining the optimal number of neurons of a hidden layer, thereby establishing the neural network based on elman, wherein the neural network based on elman comprises an input layer, the hidden layer, a receiving layer and an output layer, and the receiving layer is used for memorizing an output value at the previous moment of the hidden layer and returning the output value to the input of the hidden layer;
C. normalization processing is carried out on historical data of the generated power and historical weather parameter information, and then the established neural network is trained according to the data after normalization processing, so that the prediction error of the neural network based on elman is controlled within a preset range;
D. and (4) taking historical data of the generated power of a week before the predicted day and weather parameter data of the predicted day as input, and predicting the generated power of the predicted day by adopting the trained neural network.
Further, the preset range is 5% -10%.
Further, the historical generated power data comprises generated power per hour and an effective generated time period, and the historical weather parameter information comprises air temperature, air pressure, wind direction, wind speed, cloud amount, rainfall, sunshine time and weather type.
Further, the step B, which comprises:
b1, counting the acquired historical generated power data and historical weather parameter information, taking the actual generated power of one day as the output data of the neural network, and taking the generated power W of one hour in the effective time period f of the previous week of the day and the weather parameter data of the day as the input data of the neural network;
b2, initializing the elman neural network, and determining an input node unit vector, a hidden layer node unit vector, a feedback state vector and an output node vector according to the input and output sequence, thereby establishing an elman-based neural network training model, wherein the number of nodes of the hidden layer is obtained by a gradually increasing trial and error method.
Further, the nonlinear state space expression of the elman neural network is as follows:
，
wherein,youtputting node vectors in m dimensions;lis composed ofnDimension hidden layer node unit vectors;xis a u-dimensional input vector;cis an n-dimensional feedback state vector; w is a3Connecting the weight from the hidden layer to the output layer; w is a2Connecting the input layer to the hidden layer by a weight value; w is a1The connection weight from the bearer layer to the hidden layer;g() is the transfer function of the output neuron;f(. x) is the transfer function of the hidden layer neurons.
Further, the step C, which comprises:
c1, performing normalization processing on the historical generated power data and the historical weather parameter information by adopting a maximum and minimum method, wherein the formula of the normalization processing is as follows:
，
wherein,x maxfor the maximum number in the data sequence,x minis the smallest number in the data sequence;
and C2, performing error calculation, weight value updating and threshold value updating on the established neural network according to the data after normalization processing, so as to control the prediction error of the elman-based neural network within the range of 5% -10%.
Further, the elman neural network adopts BP algorithm to carry out weight correction updating, and adopts error square sum function to carry out index function learning, and the index functionE(w)The formula learned is:
，
wherein,a target input vector is obtained.
The invention has the beneficial effects that: the method comprises the steps that the generating power of a predicted day is obtained by constructing an Elman neural network and combining historical weather parameter information corresponding to historical generating power data of photovoltaic generating equipment in a relevant region, wherein the Elman neural network comprises an input layer, a hidden layer, an output layer and an input carrying layer, the input carrying layer is used for memorizing an output value of the hidden layer at the previous moment and returning the output value to the hidden layer, feedback is added, and the Elman neural network is sensitive to the historical data and stable; when training is carried out, error control is needed, any nonlinear mapping can be approached with any precision, the influence of external noise on the system is not considered, and therefore the system has high precision and the capability of adapting to time-varying characteristics, the characteristics of a dynamic process system can be directly and dynamically reflected, and the fluctuation of prediction precision is reduced.
Drawings
The invention is further illustrated by the following figures and examples.
FIG. 1 is an overall flow chart of a photovoltaic power generation system power prediction method based on an elman neural network according to the present invention;
FIG. 2 is a schematic diagram of the structure of the elman-based neural network of the present invention;
FIG. 3 is a flow chart of step B of the present invention;
FIG. 4 is a flow chart of step C of the present invention.
Detailed Description
Referring to fig. 1 and 2, a photovoltaic power generation system power prediction method based on an elman neural network includes:
A. acquiring historical generated power data and corresponding historical weather parameter information of photovoltaic power generation equipment in relevant areas;
B. determining input and output data of a neural network, and determining the optimal number of neurons of a hidden layer, thereby establishing the neural network based on elman, wherein the neural network based on elman comprises an input layer, the hidden layer, a receiving layer and an output layer, and the receiving layer is used for memorizing an output value at the previous moment of the hidden layer and returning the output value to the input of the hidden layer;
C. normalization processing is carried out on historical data of the generated power and historical weather parameter information, and then the established neural network is trained according to the data after normalization processing, so that the prediction error of the neural network based on elman is controlled within a preset range;
D. and (4) taking historical data of the generated power of a week before the predicted day and weather parameter data of the predicted day as input, and predicting the generated power of the predicted day by adopting the trained neural network.
Further as a preferred embodiment, the preset range is 5% to 10%.
Further preferably, the historical generated power data includes generated power per hour and an effective power generation time period, and the historical weather parameter information includes air temperature, air pressure, wind direction, wind speed, cloud cover, rainfall, sunshine time and weather type.
Referring to fig. 3, as a further preferred embodiment, the step B includes:
b1, counting the acquired historical generated power data and historical weather parameter information, taking the actual generated power of one day as the output data of the neural network, and taking the generated power W of one hour in the effective time period f of the previous week of the day and the weather parameter data of the day as the input data of the neural network;
b2, initializing the elman neural network, and determining an input node unit vector, a hidden layer node unit vector, a feedback state vector and an output node vector according to the input and output sequence, thereby establishing an elman-based neural network training model, wherein the number of nodes of the hidden layer is obtained by a gradually increasing trial and error method.
Further as a preferred embodiment, the non-linear state space expression of the elman neural network is:
，
wherein,youtputting node vectors in m dimensions;lis composed ofnDimension hidden layer node unit vectors;xis a u-dimensional input vector;cis an n-dimensional feedback state vector; w is a3Connecting the weight from the hidden layer to the output layer; w is a2Connecting the input layer to the hidden layer by a weight value; w is a1The connection weight from the bearer layer to the hidden layer;g() is the transfer function of the output neuron;f(. x) is the transfer function of the hidden layer neurons.
Referring to fig. 4, as a further preferred embodiment, the step C includes:
c1, performing normalization processing on the historical generated power data and the historical weather parameter information by adopting a maximum and minimum method, wherein the formula of the normalization processing is as follows:
，
wherein,x maxfor the maximum number in the data sequence,x minis the smallest number in the data sequence;
and C2, performing error calculation, weight value updating and threshold value updating on the established neural network according to the data after normalization processing, so as to control the prediction error of the elman-based neural network within the range of 5% -10%.
Further as a preferred embodiment, the elman neural network adopts a BP algorithm to update weight correction, and adopts an error sum of squares function to learn an index function, wherein the index functionE(w)The formula learned is:
，
wherein,a target input vector is obtained.
The invention is described in further detail below with reference to the figures and specific examples of the specification.
Example one
Referring to fig. 2, a first embodiment of the present invention:
the invention adopts a prediction model based on the Elman neural network to realize a photovoltaic systemShort-term prediction of system generated power, the structure of the Elman neural network model is shown in fig. 2. Wherein, X1，X2Xu is a node of an input layer, and corresponds to input predicted daily weather parameters and the power generation power of the photovoltaic system in the last week; y is1Is a node of an output layer, and the predicted daily system power generation power is correspondingly output; l1，l2···lNIs a node of the hidden layer, wherein the number n of the nodes of the hidden layer (i.e. the optimal number of neurons of the hidden layer) is determined according to a method of gradually increasing the heuristic; c1，C2···CNThe node of the receiving layer is used for memorizing the output value of the hidden layer unit at the previous moment and returning the output value to the input of the hidden layer.
The nonlinear state space expression of the Elman neural network is as follows:
wherein g (—) is the transfer function of the output neuron, which is a linear combination of the hidden layer outputs; f (#) is the transfer function of hidden layer neurons, and an S function is often adopted.
Example two
Referring to fig. 1-4, a second embodiment of the present invention:
the photovoltaic power generation system power prediction method mainly comprises the following implementation steps:
step 1, acquiring historical data of generated power of photovoltaic power generation equipment in relevant areas, wherein the historical data comprises generated power W per hour and an effective power generation time period f, so as to acquire an effective power generation time period of predicted generated power;
step 2, obtaining corresponding historical weather parameter information, including but not limited to air temperature T, air pressure P, wind direction WD, wind speed WS, cloud cover C, rainfall R, sunshine time T and weather type P;
step 3, counting the historical generated power data and the historical weather parameter information, taking the actual generated power of one day as the output data of the neural network, and taking the hourly generated power W and the weather parameter data of the forecast day of the previous week in the effective time period f as the input data;
step 4, initializing a network, determining a u-dimensional input node unit vector X, an n-dimensional hidden layer node unit vector l, an n-dimensional feedback state vector c and an m-dimensional output node vector Y according to the input and output sequence (X, Y), wherein the number n of hidden layer nodes determines the optimal number of hidden layer neurons according to a gradually increasing and exploring method until the network performance reaches a set threshold value or is optimal, and thus establishing an Elman-based neural network training model;
step 5, performing normalization processing on the historical data of the generated power and the historical information of the weather parameters by using a maximum and minimum method, training a neural network by using the normalized data of the generated power and the historical information of the weather parameters (including the processes of error calculation, weight updating and threshold updating), and controlling the prediction error of the network within 5-10%;
and 6, after the training is finished, generating power prediction of a prediction day can be carried out by utilizing the neural network, so that a prediction result is obtained.
Compared with the prior art, the photovoltaic power generation system power prediction method based on the Elman neural network is used for establishing the photovoltaic power generation system neural network power prediction model, can approach any nonlinear mapping with any precision, does not consider the influence of external noise on the system, enables the system to have higher precision, has the capability of adapting to time-varying characteristics, and can directly and dynamically reflect the characteristics of a dynamic process system.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A photovoltaic power generation system power prediction method based on an elman neural network is characterized in that: the method comprises the following steps:
A. acquiring historical generated power data and corresponding historical weather parameter information of photovoltaic power generation equipment in relevant areas;
B. determining input and output data of a neural network, and determining the optimal number of neurons of a hidden layer, thereby establishing the neural network based on elman, wherein the neural network based on elman comprises an input layer, the hidden layer, a receiving layer and an output layer, and the receiving layer is used for memorizing an output value at the previous moment of the hidden layer and returning the output value to the input of the hidden layer;
C. normalization processing is carried out on historical data of the generated power and historical weather parameter information, and then the established neural network is trained according to the data after normalization processing, so that the prediction error of the neural network based on elman is controlled within a preset range;
D. and (4) taking historical data of the generated power of a week before the predicted day and weather parameter data of the predicted day as input, and predicting the generated power of the predicted day by adopting the trained neural network.
2. The elman neural network-based photovoltaic power generation system power prediction method according to claim 1, wherein: the preset range is 5% -10%.
3. The elman neural network-based photovoltaic power generation system power prediction method according to claim 2, wherein: the historical data of the generated power comprises the generated power per hour and the effective generating time period, and the historical weather parameter information comprises air temperature, air pressure, wind direction, wind speed, cloud cover, rainfall, sunshine time and weather type.
4. The elman neural network-based photovoltaic power generation system power prediction method according to claim 3, wherein: the step B, which comprises:
b1, counting the acquired historical generated power data and historical weather parameter information, taking the actual generated power of one day as the output data of the neural network, and taking the generated power W of one hour in the effective time period f of the previous week of the day and the weather parameter data of the day as the input data of the neural network;
b2, initializing the elman neural network, and determining an input node unit vector, a hidden layer node unit vector, a feedback state vector and an output node vector according to the input and output sequence, thereby establishing an elman-based neural network training model, wherein the number of nodes of the hidden layer is obtained by a gradually increasing trial and error method.
5. The elman neural network-based photovoltaic power generation system power prediction method according to claim 4, wherein: the nonlinear state space expression of the elman neural network is as follows:
，
wherein,youtputting node vectors in m dimensions;lis composed ofnDimension hidden layer node unit vectors;xis a u-dimensional input vector;cis an n-dimensional feedback state vector; w is a3Connecting the weight from the hidden layer to the output layer; w is a2Connecting the input layer to the hidden layer by a weight value; w is a1The connection weight from the bearer layer to the hidden layer;g() is the transfer function of the output neuron;f(. x) is the transfer function of the hidden layer neurons.
6. The elman neural network-based photovoltaic power generation system power prediction method according to claim 4, wherein: the step C, which comprises:
c1, performing normalization processing on the historical generated power data and the historical weather parameter information by adopting a maximum and minimum method, wherein the formula of the normalization processing is as follows:
，
wherein,x maxfor the maximum number in the data sequence,x minis the smallest number in the data sequence;
and C2, performing error calculation, weight value updating and threshold value updating on the established neural network according to the data after normalization processing, so as to control the prediction error of the elman-based neural network within the range of 5% -10%.
7. The elman neural network-based photovoltaic power generation system power prediction method according to claim 6, wherein: the elman neural network adopts BP algorithm to carry out weight correction updating and adopts error square sum function to carry out index function learning, and the index functionE(w)The formula learned is:
，
wherein,a target input vector is obtained.
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