An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction

Jihan Ghanim, Maha Issa, M. Awad
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引用次数: 1

Abstract

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.
基于异常检测的非对称损耗LSTM功耗预测框架
建立一个准确的负荷预测模型,以最小的低估是至关重要的,以防止任何不希望的停电,由于电力生产不足。然而,住宅部门的电力消耗模式包含波动和异常,使其难以预测。在本文中,我们提出了多个具有不同非对称损失函数的长短期记忆(LSTM)框架,以对欠预测施加更高的惩罚。在负荷预测任务之前,我们还应用了基于密度的带噪声应用空间聚类(DBSCAN)异常检测方法,以去除任何现有的异常。考虑到天气和社会因素的影响,对来自法国、德国和匈牙利的三个考虑的数据集执行季节性分割,其中包含每小时的电力消耗、天气和日历特征。均方根误差(RMSE)结果表明,去除异常有效地降低了所有季节数据的低估和高估误差。此外,非对称损失函数和季节性分裂在一定程度上增加了高估误差,但有效地减少了低估。减少对用电量的低估对于防止可能对社区造成损害的停电至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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