标题： IDS-Net：一种用于少样本光伏功率预测的新型框架，具有可解释的动态选择和特征信息融合 显示英文标题

标题： IDS-Net: A novel framework for few-shot photovoltaic power prediction with interpretable dynamic selection and feature information fusion

摘要： 随着对可再生能源需求的增长，各国正在加速建设光伏（PV）电站。 然而，由于数据可用性有限，准确预测新建光伏电站的电力数据极具挑战性。 为此，我们提出了一种基于特征信息融合的新型可解释动态选择网络（IDS-Net），以实现准确的少样本预测。 该迁移学习框架主要由两部分组成。 在第一阶段，我们在大规模数据集上进行预训练，利用最大均值差异（MMD）选择与目标域数据分布最相似的源域数据集。 随后，使用ReliefF算法进行特征选择，减少特征冗余的影响。 然后，使用Hampel标识符（HI）对训练数据集进行异常值校正。 在IDS-Net模型中，我们首先从预测模型池中获取初始提取的特征。 接着，使用两个独立的加权通道分别确定每个子模型的可解释权重和自适应选择结果。 随后，将每个子模型的提取特征结果与其对应的权重相乘，然后求和以获得加权提取特征。 然后，我们对附加特征进行交叉嵌入，并将其与提取的加权特征融合。 此融合信息随后通过MLP（多层感知器）层以获得预测结果。 在第二阶段，我们设计了一个端到端的自适应迁移学习策略，在目标数据集上获得最终的预测结果。 我们使用中国河北省的两个光伏电站数据集验证迁移学习过程，以展示我们框架和迁移学习策略的有效性和泛化能力。 显示英文摘要

摘要： With the growing demand for renewable energy, countries are accelerating the construction of photovoltaic (PV) power stations. However, accurately forecasting power data for newly constructed PV stations is extremely challenging due to limited data availability. To this end, we propose a novel interpretable dynamic selection network (IDS-Net) based on feature information fusion to achieve accurate few-shot prediction. This transfer learning framework primarily consists of two parts. In the first stage, we pre-train on the large dataset, utilizing Maximum Mean Discrepancy (MMD) to select the source domain dataset most similar to the target domain data distribution. Subsequently, the ReliefF algorithm is utilized for feature selection, reducing the influence of feature redundancy. Then, the Hampel Identifier (HI) is used for training dataset outlier correction. In the IDS-Net model, we first obtain the initial extracted features from a pool of predictive models. Following this, two separate weighting channels are utilized to determine the interpretable weights for each sub-model and the adaptive selection outcomes, respectively. Subsequently, the extracted feature results from each sub-model are multiplied by their corresponding weights and then summed to obtain the weighted extracted features. Then, we perform cross-embedding on the additional features and fuse them with the extracted weighted features. This fused information is then passed through the MLP (Multi-Layer Perceptron) layer to obtain predictions. In the second stage, we design an end-to-end adaptive transfer learning strategy to obtain the final prediction results on the target dataset. We validate the transfer learning process using two PV power datasets from Hebei province, China, to demonstrate the effectiveness and generalization of our framework and transfer learning strategy.