| Title |
Optimizing the Training Strategy of Bi-LSTM Models for Real-Time Electricity Demand Forecasting |
| Authors |
서동혁(Dong-Hyeok Seo) ; 위영민(Young-Min Wi) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.2.258 |
| Keywords |
Real-Time Forecasting; Electricity Demand; Bi-LSTM; Training Strategy |
| Abstract |
This paper studies the optimization of training strategies for a Bi-LSTM (Bidirectional Long Short-Term Memory) model to improve real-time electricity demand forecasting accuracy. Unlike most existing research that emphasizes hyperparameter tuning, this work examines three key training strategy variables essential in practice: training period, window size, and retraining frequency. Using four years of South Korea’s power demand and weather data, a sequential search identifies the optimal configuration. Results show that a 24-month training period, a 7-day window, and a 3-day retraining cycle achieve the most accurate and stable forecasts, with an average MAPE of 0.84%. The study also assesses the trade-off between accuracy and computational cost, confirming the practicality of the proposed strategy and underscoring that training strategy optimization is as critical as model architecture tuning." |