# National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?

arXiv: https://arxiv.org/abs/2107.06174 · HTML: https://mlsanity.com/paper/2107.06174/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 115 references that are in the map.
- First posted on arXiv 30 Jun 2021.

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

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Towards Binary-Valued Gates for Robust LSTM Training](https://mlsanity.com/paper/1806.02988/) (2018)

## Built on by (most cited first, 5 of 5)

- [Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series](https://mlsanity.com/paper/2310.15555/) (2023)
- [Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting](https://mlsanity.com/paper/2307.01597/) (2023)
- [High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks](https://mlsanity.com/paper/2203.03342/) (2022)
- [Ultra-short-term solar power forecasting by deep learning and data reconstruction](https://mlsanity.com/paper/2509.17095/) (2025)
- [A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas](https://mlsanity.com/paper/2304.10686/) (2023)

## Abstract (arXiv)

As the volatility of electricity demand increases owing to climate change and electrification, the importance of accurate peak load forecasting is increasing. Traditional peak load forecasting has been conducted through time series-based models; however, recently, new models based on machine or deep learning are being introduced. This study performs a comparative analysis to determine the most accurate peak load-forecasting model for Korea, by comparing the performance of time series, machine learning, and hybrid models. Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) is used for the time series model. Artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM) are used for the machine learning models. SARIMAX-ANN, SARIMAX-SVR, and SARIMAX-LSTM are used for the hybrid models. The results indicate that the hybrid models exhibit significant improvement over the SARIMAX model. The LSTM-based models outperformed the others; the single and hybrid LSTM models did not exhibit a significant performance difference. In the case of Korea's highest peak load in 2019, the predictive power of the LSTM model proved to be greater than that of the SARIMAX-LSTM model. The LSTM, SARIMAX-SVR, and SARIMAX-LSTM models outperformed the current time series-based forecasting model used in Korea. Thus, Korea's peak load-forecasting performance can be improved by including machine learning or hybrid models.