Photovoltaic output prediction method based on dynamic modeling
A technology of output forecasting and dynamic modeling, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of power forecasting error, the model cannot adapt to the latest situation, and cannot track the latest dynamic conditions, etc., so as to improve the forecasting accuracy and improve the The effect of safety and economy
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Embodiment 1
[0022] see figure 1 and figure 2 , the present invention provides a technical solution:
[0023] A photovoltaic output prediction method based on dynamic modeling, comprising the following steps:
[0024] Step 1: Obtain the original meteorological and output data, and preprocess the original meteorological and output data to obtain high-quality model training samples with reasonable size, accurate data, and comprehensive coverage;
[0025] Step 2: Train the training samples obtained in step 1 to obtain an initial photovoltaic output prediction model;
[0026] Step 3: Set the sample screening conditions for the training samples, and set the Euclidean distance threshold of the new data relative to the initial photovoltaic output prediction model in step 2, and the data within the threshold will be screened as valid data to participate in the model Update the training, and the data exceeding the threshold will be eliminated;
[0027] Step 4: Collect new data samples, preproc...
Embodiment 2
[0031] see figure 1 and figure 2 , the present invention provides the second technical solution:
[0032] Step 1: Obtain the original meteorological and output data, and preprocess the original meteorological and output data to obtain high-quality model training samples with reasonable size, accurate data, and comprehensive coverage; the preprocessing operations of the original meteorological and output data in step 1 include Abnormal data identification, correction and sample selection are carried out on the data, and abnormal data identification, correction and sample selection are carried out in sequence. Such operations can repair and improve some errors in the data or data that do not satisfy the identification, so that the established original model It has high accuracy and provides a good reference model for establishing a new photovoltaic output model later;
[0033] Step 2: Train the training samples obtained in step 1 to obtain an initial photovoltaic output predi...
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