The rollout of smart meters and steadily increasing sample rates lead to a growing amount of raw data available for short-term load forecasting (STLF). While the original motivation for high resolutions has been the enabling of non-intrusive load monitoring (NILM), so far their value for STLF has been limited. We propose a novel approach, which allows the exploitation of high resolution data for STLF, by incorporating NILM and subsequent clustering of similarly behaving appliances as a preprocessing step.

中文翻译：

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海报摘要：基于分解的智能电表数据的电网级短期负荷预测

智能电表的推出以及不断提高的采样率导致可用于短期负荷预测（STLF）的原始数据数量不断增长。虽然高分辨率的最初动机是启用非侵入式负载监控（NILM），但到目前为止，它们对STLF的价值受到限制。我们提出了一种新颖的方法，该方法允许通过合并NILM和类似行为的设备的后续群集作为预处理步骤来利用STLF的高分辨率数据。