For the existing problems of short-term photovoltaic power interval prediction,a framework combining a time convolution neural network with an attention mechanism is proposed.This framework imposes strict constraints on the temporal causal order in the attention mechanism,applies residual blocks to enhance the information mining ability of the model,and utilizes model parameters for quality-driven interval loss simultaneously,which ultimately improves the short-term power interval prediction effect.The simulation experiments based on the local meteorological data and historical photovoltaic power data of a photovoltaic power station in Hebei Province,China,show that compared with the traditional sequence prediction method or interval loss,the power interval prediction method proposed in this paper is more effective for scientific dispatching and decision-making of the power grid in continuous time and different weather types.
关键词
光伏发电/功率预测/深度学习/时间卷积网络/因果注意力机制/质量驱动损失
Key words
PV power/power forecasting/deep learning/temporal convolutional network/causal attention mechanism/quality-driven loss
维普
万方数据