Multivariate Prediction of Photovoltaic Power Time Series Based on Deep Neural Networks
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.
photovoltaic power forecastingconvolutional neural networkdeep neural networkslong short-term memory net-work
万方数据
维普
