Combining the predictions of different base experts is a well known approach used to improve the accuracy of time series forecasts. Forecast aggregation is becoming crucial in many fields, including electricity forecasting, as Internet of Things and Cloud technology give access to larger numbers of sensor data, time series and predictions from external providers. In this context, it is not uncommon that the failure of some experts causes relevant drops in the performances of the aggregated forecast when classical techniques based on linear averaging are applied. This might be a symptom of suboptimality of the individual experts, that do not fully exploit important predictors, e.g. calendar features that play a major role in the electrical demand profiles. In this work, we therefore present two non-linear strategies to obtain aggregated forecasts, starting from the availability of a set of base experts and the knowledge of some relevant predictor variables. The first approach, called aggregation of enhanced experts (AEE), enhances each individual expert and then feeds the enhanced forecasts into classical linear aggregation techniques. In the second approach, called enhanced aggregation of experts (EAE), the expert forecasts are nonlinearly combined with the predictor variables through an Artificial Neural Network (ANN). The case of missing expert forecasts is also considered via a statistically-based imputation method. The short-term prediction of German electrical load is used as a case study. Twelve base experts are enhanced with respect to calendar features, i.e. daytime and weekday. Compared to state-of-the-art aggregation methods applied to the not-enhanced set of experts, the proposed approaches not only improve the accuracy of aggregated forecast (up to 25% reduction of MAPE and RMSE), but are also robust with respect to missing experts.

结合不同基础专家的预测是一种众所周知的方法，用于提高时间序列预测的准确性。预测聚合在许多领域变得至关重要，包括电力预测，因为物联网和云技术可以访问来自外部供应商的大量传感器数据、时间序列和预测。在这种情况下，当应用基于线性平均的经典技术时，一些专家的失败导致聚合预测的性能下降的情况并不少见。这可能是个别专家的次优症状，他们没有充分利用重要的预测因素，例如在电力需求概况中起主要作用的日历特征。因此，在这项工作中，我们提出了两种非线性策略来获得聚合预测，从一组基础专家的可用性和一些相关预测变量的知识开始。第一种方法称为增强型专家聚合 (AEE)，它增强每个单独的专家，然后将增强型预测提供给经典的线性聚合技术。在称为增强专家聚合 (EAE) 的第二种方法中，专家预测通过人工神经网络 (ANN) 与预测变量非线性组合。还通过基于统计的插补方法考虑了专家预测缺失的情况。德国电力负荷的短期预测被用作案例研究。十二个基本专家在日历功能方面得到了增强，即白天和工作日。与应用于未增强的专家组的最先进的聚合方法相比，