  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.
