用注意力模型提升电力负荷预测精度，优于传统方法。
From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
- 采用Transformer等深度模型替代传统统计方法
- 24小时预测MAPE降至3.8%，最优于ARIMA、LSTM等
- 适合电力系统调度与能源管理领域研究人员
准确的短期电力负荷预测对现代电力系统的有效管理、优化和鲁棒性至关重要。本文基于PJM每小时用电量数据，对传统统计模型与深度学习方法进行了实证评估。比较了ARIMA、LSTM、BiLSTM和Transformer四种模型在24小时预测任务中的表现。数据处理包括插值、归一化和滑动窗口序列构建。使用MAE、RMSE和MAPE指标进行评估，结果表明，基于自注意力机制的Transformer模型表现最佳，MAPE为3.8%，在准确性和鲁棒性上均优于其他模型。研究证实了注意力架构在捕捉电力消费复杂时间模式方面的巨大潜力。
原文摘要 · Abstract (English)
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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