Multivariable Time-sequential Short-term Photovoltaic Power Interval Prediction Based on Improved AT-QRCNN-BiLSTM Model
To address the challenges of random variability,fluctuation,intermittency in photovoltaic(PV)power genera-tion,and the uncertainty in weather and environmental conditions,an improved quantile regression convolution neural net-work based on attention mechanism and bidirectional long-term memory neural network short-term photovoltaic power in-terval prediction method based on multivariable time sequence was established.The method consisted of three parts:data prediction and correction,data point prediction,and interval prediction with optimization objective function.First,consid-ering the influence of measurement error on historical data,the historical data of photovoltaic power and environment were estimated and processed,and the estimated historical data were obtained.Second the estimated photovoltaic power and environmental historical data were input into a point prediction model constructed by the fusion of convolution neural network based on attention mechanism and bidirectional short-term memory neural network,and the corrected point pre-diction results were obtained.Then the upper and lower bounds of the confidence interval were predicted by the improved beluga whale optimization algorithm.Finally the prediction results were analyzed by the evaluation index of interval predic-tion.The simulation results showed that the proposed hybrid network model can achieve high accuracy with fast conver-gence speed,and effectively solve the problem of serious impact of stochasticity,uncertainty and time-sequence on photo-voltaic power prediction by improving prediction accuracy.
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