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arXiv · 2603.06622

From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning

Suhasnadh Reddy Veluru·Sai Teja Erukude·Viswa Chaitanya Marella
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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.

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Suhasnadh Reddy Veluru, Sai Teja Erukude, Viswa Chaitanya Marella. 2026-02-21. From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning. https://doi.org/10.1109/giest66547.2025.11387354

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