An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition
This work addresses forecasting accuracy for power system planning, but it is incremental as it builds on existing decomposition and ensemble techniques.
The paper tackled error accumulation in mid-term electricity load forecasting by proposing an error correction model (ECLF) that uses seasonal decomposition and ensemble methods, achieving superior performance on real-world data from two Chinese cities.
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.