论文标题
在线层次结构预测功耗数据
Online Hierarchical Forecasting for Power Consumption Data
论文作者
论文摘要
我们研究了对家庭及其亚群体人口的电力消耗的预测。这些亚群是根据位置，外源信息和/或从历史悠久的家庭消费时间序列确定的。因此，我们的目标是预测多个家庭聚集的电力消耗时间序列。这些时间序列通过一些诱导层次结构的总和约束链接。我们的方法包括三个步骤：特征产生，聚合和投影。首先（功能生成步骤），我们为家庭的每个考虑组建立了一个基准预测（称为功能），使用随机森林或广义添加剂模型。其次（聚合步骤），聚合算法并行运行，汇总这些预测并提供新的预测。最后（投影步骤），我们使用由层次结构基础的时间序列引起的求和约束来通过将其投影在精心挑选的线性子空间中来调和预测。我们通过最小化称为“遗憾的数量”来对该方法的平均预测错误提供一些理论保证。我们还在能源需求研究项目环境中由多个能源提供者在英国收集的家庭用电量数据测试我们的方法。我们构建和比较各种人口细分以评估我们的方法性能。
We study the forecasting of the power consumptions of a population of households and of subpopulations thereof. These subpopulations are built according to location, to exogenous information and/or to profiles we determined from historical households consumption time series. Thus, we aim to forecast the electricity consumption time series at several levels of households aggregation. These time series are linked through some summation constraints which induce a hierarchy. Our approach consists in three steps: feature generation, aggregation and projection. Firstly (feature generation step), we build, for each considering group for households, a benchmark forecast (called features), using random forests or generalized additive models. Secondly (aggregation step), aggregation algorithms, run in parallel, aggregate these forecasts and provide new predictions. Finally (projection step), we use the summation constraints induced by the time series underlying hierarchy to re-conciliate the forecasts by projecting them in a well-chosen linear subspace. We provide some theoretical guaranties on the average prediction error of this methodology, through the minimization of a quantity called regret. We also test our approach on households power consumption data collected in Great Britain by multiple energy providers in the Energy Demand Research Project context. We build and compare various population segmentations for the evaluation of our approach performance.
