This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of a lower resolution (30 minutes). We combine predictions of generalized additive models (GAM) and deep artificial neural networks (DNN), which are popular in load forecasting. We extensively analyze the prediction models, including the input parameters' importance, focusing on load, weather, and seasonal effects. The proposed method won a data competition organized by Western Power Distribution, a British distribution network operator. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models' robustness. The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error (RMSE). This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months. Overall, our proposed model combination reduces the out-of-sample RMSE by 57.4\% compared to the benchmark.
翻译:本文涵盖预测高分辨率电峰需求特征的模型,其中给出了分辨率较低的数据。这是一个相关的设置,因为它回答了有限的高分辨率监测是否有助于在高分辨率数据不再可用时估计未来高分辨率峰值负荷的问题。这个问题对于网络运营商出于经济考虑考虑考虑取代高分辨率监测预测模型尤其有趣。我们提出了预测半个小时高分辨率(每分钟)电荷数据模型和峰值模型的模型,而模型输入的分辨率较低(30分钟)。我们结合了对通用添加模型(GAM)和深层人工神经网络(DNN)的预测,这些模型在载荷预测中很受欢迎。我们广泛分析了预测模型,包括输入参数的重要性,重点是负荷、天气和季节效应。拟议方法赢得了由英国配送网络运营商Wester Pow Power Sulvement组织的数据竞争。此外,我们提供了一项严格的评价研究,该研究超越竞争框架,分析了模型的强度。结果显示,拟议的方法优于关于负重根中值平均误差的竞争基准。我们广泛分析了预测模型(ARNNNNN),这比整个竞争基准要长一个11个月。