PHOTOVOLTAIC POWER PREDICTION BASED ON LSTM-ATTENTION AND CNN-BiGRU ERROR CORRECTION
To effectively analyze and utilize the prediction errors distributed in a specific pattern in the photovoltaic power prediction model,a photovoltaic power prediction model based on LSTM-Attention and CNN-BiGRU error correction is proposed.Firstly,the LSTM-Attention mechanism is introduced to compensate for the shortcomings of the long short-term memory(LSTM)network,which is difficult to retain key information in the input sequence.Secondly,the advantages of convolutional neural network(CNN)in non-linear feature extraction are combined with the advantages of Bidirectional gated recurrent unit(BiGRU)in preventing multiple features from interfering with each other to build a CNN-BiGRU error prediction model is used to predict the possible errors and to correct the initial prediction results.The experimental results indicates that the CNN-BiGRU error prediction model can effectively improve the prediction accuracy in different weather types when compared with the prediction results without error correction.
photovoltaic power predictiondeep learningerror correctionattention mechanismlong-short term memory networkbidirectional gated recurrent unit
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