This article presents a new spatio-temporal framework for the day-ahead probabilistic forecasting of Distribution Locational Marginal Prices (DLMPs). The approach relies on a recurrent neural network, whose architecture is enriched by introducing a deep bidirectional variant designed to capture the complex time dynamics in multi-step forecasts. In order to account for nodal price differentiation (arising from grid constraints) within a procedure that is scalable to large distribution systems, nodal DLMPs are predicted individually by a single model guided by a generic representation of the grid. This strategy offers the additional benefit to enable cold-start forecasting for new nodes with no history. Indeed, in case of topological changes, e.g., building of a new home or installation of photovoltaic panels, the forecaster intrinsically leverages the statistical information learned from neighbouring nodes to predict the new DLMP, without needing any modification of the tool. The approach is evaluated, along with several other methods, on a radial low voltage network. Outcomes highlight that relying on a compact model is a key component to boost its generalization capabilities in high-dimensionality, while indicating that the proposed tool is effective for both temporal and spatial learning.

中文翻译：

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在分布位置边际价格概率预测中捕获时空相关性

本文提出了一种新的时空框架，用于进行分布位置边际价格（DLMP）的日前概率预测。该方法依赖于递归神经网络，该神经网络的结构通过引入深层次的双向变量来丰富，该变量旨在捕获多步预测中的复杂时间动态。为了在可扩展到大型配电系统的过程中解决节点价格差异（由网格约束引起），节点DLMP由单个模型单独预测，该模型由网格的通用表示指导。该策略提供了额外的好处，可以对没有历史记录的新节点进行冷启动预测。确实，在拓扑变化的情况下（例如，建造新房屋或安装光伏面板），预测器从本质上利用从相邻节点学到的统计信息来预测新的DLMP，而无需对工具进行任何修改。该方法与其他几种方法一起在径向低压网络上进行了评估。结果强调依赖紧凑模型是提高其高维泛化能力的关键组成部分，同时表明所提出的工具对于时间和空间学习都是有效的。