Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.

翻译：电力需求预测在保障电力供应可靠且经济高效运行中发挥着关键作用。随着全球向分布式可再生能源转型以及供暖和交通领域的电气化发展，准确的负荷预测变得愈发重要。尽管已有大量实证研究和少量综述文章，但对文献的定量分析却出人意料地匮乏，尤其是缺乏一项能够综合所有实证研究、识别影响预测性能因素的分析。因此，本文提出了一种元回归分析（MRA），旨在考察影响短期电力负荷预测准确性的因素。我们使用了来自59项研究中421个预测模型的数据。结果表明，电网层级（尤其是个体、聚合与系统级）、预测粒度以及所用算法对平均绝对百分比误差（MAPE）具有显著影响，而文献计量数据、数据集规模和预测期则未表现出显著效应。我们发现LSTM方法以及神经网络与其他方法的组合是最优预测方法。这些结果有助于实践者和研究人员做出有意义的模型选择。然而，本文也呼吁在负荷预测领域开展更多元回归分析，以填补负荷预测研究与实践中的盲区。