  Mid-term electricity load forecasting (LF) plays a critical role in power
system planning and operation. To address the issue of error accumulation and
transfer during the operation of existing LF models, a novel model called error
correction based LF (ECLF) is proposed in this paper, which is designed to
provide more accurate and stable LF. Firstly, time series analysis and feature
engineering act on the original data to decompose load data into three
components and extract relevant features. Then, based on the idea of stacking
ensemble, long short-term memory is employed as an error correction module to
forecast the components separately, and the forecast results are treated as new
features to be fed into extreme gradient boosting for the second-step
forecasting. Finally, the component sub-series forecast results are
reconstructed to obtain the final LF results. The proposed model is evaluated
on real-world electricity load data from two cities in China, and the
experimental results demonstrate its superior performance compared to the other
benchmark models.
