The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales. In this work, we take advantage of the similarity between this hierarchical prediction problem and multi-scale transfer learning. We develop two methods for hierarchical transfer learning, based respectively on the stacking of generalized additive models and random forests, and on the use of aggregation of experts. We apply these methods to two problems of electricity load forecasting at national scale, using smart meter data in the first case, and regional data in the second case. For these two usecases, we compare the performances of our methods to that of benchmark algorithms, and we investigate their behaviour using variable importance analysis. Our results demonstrate the interest of both methods, which lead to a significant improvement of the predictions.
翻译:最近不同规模的电力消费数据丰富,带来了新的挑战,并突出表明需要采用新技术,利用更精细规模的现有信息,改进更大规模的预测。在这项工作中,我们利用这一等级预测问题与多尺度转移学习的相似性。我们开发了两种等级转移学习方法,分别基于通用添加模型和随机森林的堆叠和随机森林,以及专家集成的两种方法。我们将这些方法应用于国家规模的电力负荷预测的两个问题,第一是使用智能计量数据,第二是使用区域数据。在这两个使用案例中,我们比较了我们方法的性能与基准算法的性能,我们用不同的重要性分析来调查它们的行为。我们的结果显示了两种方法的兴趣,这导致对预测的显著改进。