From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
Abstract
Accurate short-term power load forecasting is important to effectively manage, optimize, and ensure the robustness of modern power systems. This paper performs an empirical evaluation of a traditional statistical model and deep learning approaches for predicting short-term energy load. Four models, namely ARIMA, LSTM, BiLSTM, and Transformer, were leveraged on the PJM Hourly Energy Consumption data. The data processing involved interpolation, normalization, and a sliding-window sequence method. Each model's forecasting performance was evaluated for the 24-hour horizon using MAE, RMSE, and MAPE. Of the models tested, the Transformer model, which relies on self-attention algorithms, produced the best results with 3.8 percent of MAPE, with performance above any model in both accuracy and robustness. These findings underscore the growing potential of attention-based architectures in accurately capturing complex temporal patterns in power consumption data.
Cite
@article{arxiv.2603.06622,
title = {From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning},
author = {Suhasnadh Reddy Veluru and Sai Teja Erukude and Viswa Chaitanya Marella},
journal= {arXiv preprint arXiv:2603.06622},
year = {2026}
}
Comments
5 pages; Published in IEEE