Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting
This work addresses the lack of quantitative analysis in load forecasting literature, providing insights for practitioners and researchers to improve model selection, though it is incremental as it applies existing meta-regression methods to this domain.
The authors conducted a meta-regression analysis to identify factors affecting the accuracy of short-term electricity load forecasts, using data from 421 forecast models across 59 studies, and found that grid level, forecast granularity, and algorithms significantly impact MAPE, with LSTM and hybrid neural networks performing best.
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.