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.
Abstract (translated)
准确的短期电力负荷预测对于有效管理、优化和确保现代电力系统的鲁棒性至关重要。本文对传统统计模型和深度学习方法在预测短期能源负载方面的性能进行了实证评估。研究中使用了PJM小时用电量数据，测试了四种模型：ARIMA、LSTM、BiLSTM 和 Transformer。数据处理包括插值、归一化以及滑动窗口序列法。每种模型的预测性能均通过24小时范围内的MAE（平均绝对误差）、RMSE（均方根误差）和MAPE（平均绝对百分比误差）进行了评估。 在所有测试的模型中，基于自注意力算法的Transformer模型表现最佳，其MAPE值为3.8%，不仅准确率高于其他任何一种模型，在鲁棒性方面也同样表现出色。这些发现强调了基于注意力机制的架构在捕捉电力消耗数据中的复杂时间模式方面的巨大潜力和价值。
URL
https://arxiv.org/abs/2603.06622
