AI RESEARCH
From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
arXiv CS.LG
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ArXi:2603.06622v1 Announce Type: new 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.