Abstract Increasing digitization of the electric power sector allows to further rethink forecasting problems that are crucial input to decision-making. Among other modern challenges, ensuring coherency of forecasts among various agents and at various aggregation levels has recently attracted attention. A number of reconciliation approaches have been proposed, from both game-theoretical and statistical points of view. However, most of these approaches make unrealistic unbiasedness assumptions and overlook the fact that the underlying stochastic processes may be nonstationary. We propose here an alternative approach to the forecast reconciliation problem in a constrained regression framework. This relies on a multivariate least squares estimator, with equality constraints on the coefficients (denoted MLSE). A recursive and adaptive version of that estimator is derived (denoted MRLSE), hence allowing to track the optimal reconciliation in a fully data-driven manner. We also prove that our methods by design guarantee the coherency property for any out-of-sample forecasts (reconciliation by design). We show the effectiveness of our forecasting methods using a Danish wind energy dataset with 100 wind farms.

中文翻译：

---

风电预测中的在线预测对账

摘要 电力部门日益数字化允许进一步重新思考对决策至关重要的预测问题。在其他现代挑战中，确保各种代理之间和各种聚合级别的预测的一致性最近引起了人们的注意。从博弈论和统计的角度来看，已经提出了许多协调方法。然而，这些方法中的大多数都做出了不切实际的无偏假设，并忽略了潜在随机过程可能是非平稳的这一事实。我们在这里提出了一种在约束回归框架中解决预测协调问题的替代方法。这依赖于多元最小二乘估计器，对系数具有等式约束（表示为 MLSE）。推导出该估计器的递归和自适应版本（表示为 MRLSE），因此允许以完全数据驱动的方式跟踪最佳对账。我们还证明，我们的设计方法保证了任何样本外预测的一致性（设计协调）。我们使用包含 100 个风电场的丹麦风能数据集展示了我们的预测方法的有效性。