Photovoltaic (PV) power probabilistic forecasting that provides decision makers with probabilistic information and ranges of PV power generation is critical to the power system. Existing studies have demonstrated that QR-based nonlinear models can generate probability distributions directly from historical data. However, the accuracy of these methods may be degraded when confronting with PV power at high latitude meteorological factors and they inherently have flaws in the model structure and loss function. This paper proposes a novel approach called monotonic quantile convolutional neural network-multi-layer nondominated fast sort genetic algorithm II (MQCNN-MLNSGAII) for solving these challenges. MQCNN first uses the convolutional structure to extract the valid deep features from the high latitude factor, and then designs a monotonic quantile structure to output monotonically increasing probability distributions at once. Considering the high impact of the probability distribution width on the quality of the forecasting, we design two loss functions, average quantile loss (AQS) and quantile distribution average width (QDAW), based on multi-objective optimization (MOO) to balance the reliability and width. Finally, a novel multi-objective evolutionary algorithm (MOEA), MLNSGAII, is proposed for training MQCNN. It develops a multi-layer mechanism based on global and historical information to assist the algorithm in generating diverse offspring and improve the performance in convergence and diversity. Compared to the benchmark models, the proposed model achieves significant strengths in the real Australian dataset.

中文翻译：

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一种基于单调分位数卷积神经网络和多目标优化的新型光伏功率概率预测模型


光伏 （PV） 发电概率预测为决策者提供光伏发电的概率信息和范围，对电力系统至关重要。现有研究表明，基于 QR 的非线性模型可以直接从历史数据中生成概率分布。然而，这些方法在面对高纬度气象因素下的光伏发电时，其精度可能会降低，并且它们本身在模型结构和损失函数方面存在缺陷。本文提出了一种称为单调分位数卷积神经网络-多层非支配快速排序遗传算法 II （MQCNN-MLNSGAII） 的新方法来解决这些挑战。MQCNN 首先使用卷积结构从高纬度因子中提取有效的深度特征，然后设计一个单调分位数结构，以一次输出单调递增的概率分布。考虑到概率分布宽度对预测质量的较大影响，我们基于多目标优化 （MOO） 设计了两个损失函数，平均分位数损失 （AQS） 和分位数分布平均宽度 （QDAW），以平衡可靠性和宽度。最后，提出了一种新的多目标进化算法 （MOEA） MLNSGAII 用于训练 MQCNN。它开发了基于全局和历史信息的多层机制，以辅助算法产生多样化的后代，并提高收敛和多样性的性能。与基准模型相比，所提出的模型在真实的澳大利亚数据集中取得了显著的优势。