基于QD和因果注意力TCN的光伏功率区间预测

PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON QD AND CAUSAL ATTENTION TCN

针对现有短期光伏功率区间预测问题,提出一种时间卷积神经网络与注意力机制结合的框架,对注意力机制中的时间因果顺序进行严格限制,应用残差机制增强模型挖掘的信息能力,并利用质量驱动区间损失优化模型参数,最终实现短期功率区间预测效果的提高。根据中国河北省某光伏电站的当地气象数据和历史光伏功率数据进行的仿真实验表明,相较于传统的序列预测方法或区间损失,在连续时刻和不同天气类型情况下,所提出的功率区间预测方法效果更有助于电网的科学调度与决策。

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.