High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks
利用广义加性模型与深度神经网络估计高分辨率用电峰值AI 解读 ↓
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利用广义加性模型与深度神经网络估计高分辨率用电峰值
- 研究问题
- 在仅有半小时分辨率输入、未来无法持续获取分钟级监测数据时，能否预测各半小时内分钟负荷的最大值与最小值？
- 方法与思路
- 作者组合广义加性模型与深度神经网络预测，考察负荷、天气和季节因素，并在竞赛月份及额外十一个月进行评估。
- 材料陈述的贡献
- 摘要称组合模型相对竞赛基准降低样本外均方根误差百分之五十七点四，并报告竞赛获胜及扩展评估中的表现。
- 阅读关注点
- 应核对高分辨率训练数据的可用范围、预测时点和数据泄漏控制、组合权重，以及误差降幅的计算口径与基准设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未给出误差不确定性、详细数据划分或跨地区推广证据。
原文摘要
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.