Wireless networks (e.g., 5G networks) enable distributed energy infrastructures to be connected even when they are geometrically isolated. Intelligent monitoring from remote sites therefore becomes possible, allowing decision makers to examine the status of distributed energy infrastructures from a central location. The major challenge is when local devices cannot perform the monitoring independently; transmitting every signal back to the central server triggers enormous amounts of wireless communication. To address this, we propose a two-tier AI system by offloading computations to multiple devices. Specifically, we build lightweight AI models for deployment on edge clients (i.e., edge sensors) and a large-scale AI model for the central server. These two types of AI models are trained with different criteria: the models on the edges act as the filtering tools to detect abnormal events and maximally avoid making false negative predictions, whereas the server model is supposed to be an expert for accurate predictions. By validating on a power theft dataset, we show that such a cascading methodology could filter out sufficient negative examples on the edge side while still being able to provide precise predictions on the second-round analysis.

中文翻译：

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能源基础设施的分布式监控：无线网络的两层分析


无线网络（例如 5G 网络）使分布式能源基础设施即使在几何隔离的情况下也能相互连接。因此，远程站点的智能监控成为可能，使决策者能够从中央位置检查分布式能源基础设施的状态。主要挑战是本地设备无法独立进行监控；将每个信号传输回中央服务器会触发大量的无线通信。为了解决这个问题，我们提出了一个两层人工智能系统，将计算卸载到多个设备。具体来说，我们构建了用于部署在边缘客户端（即边缘传感器）的轻量级人工智能模型和用于中央服务器的大规模人工智能模型。这两类人工智能模型的训练标准不同：边缘模型充当过滤工具，检测异常事件并最大限度地避免做出错误的负面预测，而服务器模型应该是准确预测的专家。通过对窃电数据集进行验证，我们表明这种级联方法可以过滤掉边缘侧足够的负面示例，同时仍然能够对第二轮分析提供精确的预测。