论文标题
使用较少的数据和积极的深度学习，增强了时空的电力负载预测
Enhanced spatio-temporal electric load forecasts using less data with active deep learning
论文作者
论文摘要
反对全球变暖和减轻气候变化的有效方法是使我们的能源部门振奋，并从可再生风和太阳能中提供电力。电力负载的时空预测对于计划这种过渡而变得越来越重要，而深度学习预测模型为其提供了越来越准确的预测。但是，通常使用被动学习方法随机收集用于培训深度学习模型的数据。这自然会导致对智能电表等传感器的数据和相关成本的巨大需求，从而在脱碳的电网中为电力公司带来了巨大的障碍。在这里，我们测试主动学习，在其中利用其他计算来收集更多信息的数据子集。我们展示了电力公司如何应用主动学习来更好地分发智能电表并收集其数据，以更准确地预测负载，而数据与应用被动学习相比，数据约有一半。
An effective way to oppose global warming and mitigate climate change is to electrify our energy sectors and supply their electric power from renewable wind and solar. Spatio-temporal predictions of electric load become increasingly important for planning this transition, while deep learning prediction models provide increasingly accurate predictions for it. The data used for training deep learning models, however, is usually collected at random using a passive learning approach. This naturally results in a large demand for data and associated costs for sensors like smart meters, posing a large barrier for electric utilities in decarbonizing their grids. Here, we test active learning where we leverage additional computation for collecting a more informative subset of data. We show how electric utilities can apply active learning to better distribute smart meters and collect their data for more accurate predictions of load with about half the data compared to when applying passive learning.
