Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data.

中文翻译：

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神经网络是否足以使用智能电表数据对建筑物和配电系统进行大规模的日前负荷预测

电力系统的运行越来越依赖于对本地分散负荷的大量日前预测，例如单个建筑物，微电网和小型配电系统区域。各种数据驱动模型可以有效地提前一步预测特定时间序列。这项工作的目的是研究神经网络方法是否可以提前预测整个负载曲线，并评估其在局部负载上的大规模应用的性能。为此，我们将来自其他短期负荷预测问题的网络用于多步预测。我们评估了各种前馈和递归神经网络体系结构，并从大量居民建筑物样本中得出了统计相关的结论。我们的结果表明，当我们无法预测手动设置的大量不同特性的负载时，神经网络方法可能会选择不当。本文敦促考虑其他旨在使用大规模智能电表数据替代标准化负载曲线的技术。