In this paper,two different multivariate long-and short-term memory(LSTM)network PV output power prediction methods are proposed based on deep neural networks(DNNs)by utilizing the correlation and dependence between different time series,with full consideration of the correlation characteristics between air temperature,wind speed and other influencing fac-tors.Taking the PV power plant operation data as an example,by training and testing the PV power prediction model and com-paring the results with those of the univariate LSTM and Stacked-LSTM models,the results of the study show that the pro-posed Conv-LSTM can improve the accuracy by 0.71％～1.33％on the basis of reducing the training time by 30.76％,and the Conv-LSTM and Multi-LSTM track real PV with up to 93.12％and 96.12％accuracy.
关键词
光伏发电预测/卷积神经网络/深度神经网络/长短期记忆网络
Key words
photovoltaic power forecasting/convolutional neural network/deep neural networks/long short-term memory net-work
维普
万方数据