Building an accurate load forecasting model with minimal underpredictions is
vital to prevent any undesired power outages due to underproduction of
electricity. However, the power consumption patterns of the residential sector
contain fluctuations and anomalies making them challenging to predict. In this
paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with
different asymmetric loss functions to impose a higher penalty on
underpredictions. We also apply a density-based spatial clustering of
applications with noise (DBSCAN) anomaly detection approach, prior to the load
forecasting task, to remove any present oultiers. Considering the effect of
weather and social factors, seasonality splitting is performed on the three
considered datasets from France, Germany, and Hungary containing hourly power
consumption, weather, and calendar features. Root-mean-square error (RMSE)
results show that removing the anomalies efficiently reduces the
underestimation and overestimation errors in all the seasonal datasets.
Additionally, asymmetric loss functions and seasonality splitting effectively
minimize underestimations despite increasing the overestimation error to some
degree. Reducing underpredictions of electricity consumption is essential to
prevent power outages that can be damaging to the community.

通过应用多种 LSTM 模型和基于密度的空间应用噪声聚类（DBSCAN）异常值检测方法，剔除异常值，考虑到气象和社会因素，对三个考虑了每小时用电量，天气和日历特征数据集进行季节性划分，在所有季节性数据集中有效地减少了低估和高估误差，降低用电消耗的低估预测对于防止对社区造成损害的电力中断至关重要。