# An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition

arXiv: https://arxiv.org/abs/2306.10826 · HTML: https://mlsanity.com/paper/2306.10826/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 50 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [N-BEATS neural network for mid-term electricity load forecasting](https://mlsanity.com/paper/2009.11961/) (2020)
- [Probabilistic Load Forecasting Based on Adaptive Online Learning](https://mlsanity.com/paper/2011.14721/) (2020)

## Built on by (most cited first, 2 of 2)

- [Concept Factorization via Self-Representation and Adaptive Graph Structure Learning](https://mlsanity.com/paper/2505.03390/) (2025)
- [MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2607.01627/) (2026)

## Abstract (arXiv)

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.