Abstract The application of wind power interval prediction for power systems attempts to give more comprehensive support to dispatchers and operators of the grid. Lower upper bound estimation (LUBE) method is widely applied in interval prediction. However, the existing LUBE approaches are trained by meta-heuristic optimization, which is either time-consuming or show poor effect when the LUBE model is complex. In this paper, a deep interval prediction method is designed in the framework of LUBE and an efficient gradient descend (GD) training approach is proposed to train the LUBE model. In this method, the long short-term memory is selected as a representative to show the modelling approach. The architecture of the proposed model consists of three parts, namely the long short-term memory module, the fully connected layers and the rank ordered module. Two loss functions are specially designed for implementing the GD training method based on the root mean square back propagation algorithm. To verify the performance of the proposed model, conventional LUBE models, as well as popular statistic interval prediction models are compared in numerical experiments. The results show that the proposed approach performs best in terms of effectiveness and efficiency with average 45% promotion in quality of prediction interval and 66% reduction of time consumptions compared to traditional LUBE models.

中文翻译：

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基于梯度下降优化的深度学习模型风电短期区间预测

摘要 风电区间预测在电力系统中的应用，试图为电网调度员和运营商提供更全面的支持。下上限估计（LUBE）方法广泛应用于区间预测。然而，现有的 LUBE 方法是通过元启发式优化训练的，当 LUBE 模型复杂时，这要么耗时要么效果不佳。本文在LUBE的框架下设计了一种深度区间预测方法，并提出了一种高效的梯度下降（GD）训练方法来训练LUBE模型。在该方法中，选择长短期记忆作为代表来展示建模方法。所提出模型的架构由三部分组成，即长短期记忆模块，全连接层和排序模块。两个损失函数是专门为实现基于均方根反向传播算法的GD训练方法而设计的。为了验证所提出模型的性能，在数值实验中比较了传统的 LUBE 模型以及流行的统计区间预测模型。结果表明，与传统的 LUBE 模型相比，所提出的方法在有效性和效率方面表现最佳，预测间隔质量平均提升 45%，时间消耗减少 66%。以及流行的统计区间预测模型在数值实验中进行了比较。结果表明，与传统的 LUBE 模型相比，所提出的方法在有效性和效率方面表现最佳，预测间隔质量平均提升 45%，时间消耗减少 66%。以及流行的统计区间预测模型在数值实验中进行了比较。结果表明，与传统的 LUBE 模型相比，所提出的方法在有效性和效率方面表现最佳，预测间隔质量平均提升 45%，时间消耗减少 66%。