Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect
Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect
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DOI：
10.1109/tsg.2022.3198326
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发表时间：
2023-01
影响因子：
9.6
通讯作者：
Huseyin Burak Akyol;C. Preist;Daniel Schien
中科院分区：
文献类型：
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作者：
Huseyin Burak Akyol;C. Preist;Daniel Schien
预测国内用电量对于支持网络运行、电网稳定和需求侧管理的各种现代电力系统解决方案和智能应用非常重要，其中大多数都依赖于稳健和准确的预测。产生这些预测的方法是从历史数据的统计规律中推断未来的负荷。如果缺乏这种规律性，那么预测就会回归到输入集中使用的最近观察到的消费值。然后，预测会比实际负载数据晚一步，这可能会影响预测的健壮性和应用程序的功能。目前的评估方法没有检测到这种行为，这可能导致对预测结果的过度自信。在本研究中，我们I)定义并系统地分析了这种行为，我们将其标记为持久性预测效应并说明了其影响，II)提出了一种称为1-步移的新方法来检测其存在，III)分析并建立了数据不规则性与效应之间的关系。此外，我们提供了一个案例研究，将最先进的预测技术应用于来自69个家庭的真实电力消费数据集，以证明持续预测效应、其含义以及它与历史数据统计规律的关系。
Forecasting domestic electricity consumption is important for a wide range of modern power system solutions and smart applications that support network operation, grid stability, and demand-side management, most of which depend on robust and accurate predictions. The methods producing these predictions infer future load from statistical regularity in historical data. If such regularity is lacking, predictions then regress towards the most recently observed consumption value used in the input set. Predictions then follow the actual load data one step behind in time, potentially affecting the robustness of predictions and functionality of applications. Current evaluation methods do not detect this behaviour which may result in overconfidence in prediction results. In this study, we I) define and systematically analyse this behaviour, which we label the Persistence Forecast Effect and illustrate its impacts, II) propose a novel method, called 1-Step-Shifting, to detect its presence, and III) analyse and establish the relationship between irregularity in data and the effect. Further, we provide a case study applying state-of-the-art forecasting techniques to a real-world dataset of electricity consumption data from 69 households in order to demonstrate the Persistence Forecast Effect, its implications, and its relationship to statistical regularity in historical data.