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.

本文使用元回归分析方法对电力负荷短期预测精度的影响因素进行了研究，发现预测方法、预测粒度和预测时间步长对 MAPE 的影响较大，本文最终确定 LSTM 方法和神经网络与其他方法的组合是最佳预测方法，同时也提出了在负荷预测领域中需要进一步进行实证分析的观点。

短期电力负荷预测误差的元回归分析

Meta-Regression Analysis of Errors in Short-Term Electricity Load  Forecasting

Currently the UK Electric market is guided by load (demand) forecasts
published every thirty minutes by the regulator. A key factor in predicting
demand is weather conditions, with forecasts published every hour. We present
HYENA: a hybrid predictive model that combines feature engineering (selection
of the candidate predictor features), mobile-window predictors and finally LSTM
encoder-decoders to achieve higher accuracy with respect to mainstream models
from the literature. HYENA decreased MAPE loss by 16\% and RMSE loss by 10\%
over the best available benchmark model, thus establishing a new state of the
art for the UK electric load (and price) forecasting.

HYENA 是一种混合预测模型，结合了特征工程、移动窗口预测器和 LSTM 编码器解码器，用于准确预测英国电力市场的负荷与价格，相较于以往模型可以减少 16% 的 MAPE 损失和 10% 的 RMSE 损失。