Abstract The outputs of photovoltaic (PV) power are random and uncertain due to the variations of meteorological elements, which may disturb the safety and stability of power system operation. Hence, precise day-ahead PV power forecasting is crucial in renewable energy utilization, as it is beneficial to power generation schedule and short-term dispatch of the PV integrated power grid. In this study, a novel day-ahead PV power forecasting approach based on deep learning is proposed and validated. Firstly, two novel deep convolutional neural networks (CNNs), i.e. residual network (ResNet) and dense convolutional network (DenseNet), are introduced as the core models of forecasting. Secondly, a new data preprocessing is proposed to construct input feature maps for the two novel CNNs, which involves historical PV power series, meteorological elements and numerical weather prediction. Thirdly, a meta learning strategy based on multi-loss-function network is proposed to train the two deep networks, which can ensure a high robustness of the extracted convolutional features. Owing to the learning strategy and unique architectures of the two novel CNNs, they are designed into relatively deep architectures with superb nonlinear representation abilities, which consist of more than ten layers. Both point and probabilistic forecasting results are provided in the case study, demonstrating the accuracy and reliability of the proposed forecasting approach.

中文翻译：

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基于深度卷积神经网络和元学习的日前光伏功率预测方法

摘要 由于气象要素的变化，光伏（PV）功率的输出具有随机性和不确定性，可能干扰电力系统运行的安全性和稳定性。因此，精确的日前光伏功率预测对于可再生能源利用至关重要，因为它有利于光伏并网的发电调度和短期调度。在这项研究中，提出并验证了一种基于深度学习的新型日前光伏功率预测方法。首先，介绍了两种新颖的深度卷积神经网络（CNN），即残差网络（ResNet）和密集卷积网络（DenseNet）作为预测的核心模型。其次，提出了一种新的数据预处理来为两个新颖的 CNN 构建输入特征图，其中涉及历史光伏功率序列，气象要素和数值天气预报。第三，提出了一种基于多损失函数网络的元学习策略来训练两个深度网络，可以保证提取的卷积特征的高度鲁棒性。由于这两种新型 CNN 的学习策略和独特的架构，它们被设计成相对较深的架构，具有极好的非线性表示能力，由十多个层组成。案例研究中提供了点和概率预测结果，证明了所提出的预测方法的准确性和可靠性。由于这两种新型 CNN 的学习策略和独特的架构，它们被设计成相对较深的架构，具有极好的非线性表示能力，由十多个层组成。案例研究中提供了点预测和概率预测结果，证明了所提出的预测方法的准确性和可靠性。由于这两种新型 CNN 的学习策略和独特的架构，它们被设计成相对较深的架构，具有极好的非线性表示能力，由十多个层组成。案例研究中提供了点预测和概率预测结果，证明了所提出的预测方法的准确性和可靠性。