发表机构
Linköping University（林雪平大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建智能电表能耗预测的9种深度时间序列模型实证基准，评估历史输入长度、预测时域等因素的影响，发现深度学习模型优于经典基线，轻量级架构性价比高，架构差异仅在特定场景显著。
AI 中文摘要
准确的能耗预测对电力系统的高效运行至关重要，直接影响运营成本、能源管理和系统维护。由于智能电表提供了大量高分辨率能耗数据，数据驱动方法已被用于短期和长期预测。然而，这些方法在真实智能电表数据上的对比性能仍未得到充分研究。本文提出了一个包含9种现代深度学习时间序列预测模型的实证基准，涵盖线性模型、基于MLP的模型、卷积模型和Transformer架构。我们在两个公开可用的智能电表数据集上评估这些模型，分析重点关注三个对预测性能有重大影响的因素：历史输入长度、预测时域和模型架构选择。结果显示，延长历史上下文可提升预测准确率，但仅到饱和点为止，超过该点后额外输入带来的收益有限；相反，预测时域越长，准确率越低。我们还研究了预测准确率与计算复杂度之间的权衡，评估了模型间性能差异的统计显著性和实际幅度。结果表明，深度学习模型始终优于经典基线模型，而轻量级架构在计算成本显著更低的情况下能实现相近的性能；架构差异仅在较长预测时域和异质性更高的数据集上才会显现。最后，按地理人口和家庭类别进行的子组分析显示，模型选择对大多数人口群体的影响有限。
英文摘要
Accurate forecasting of energy consumption is important for the efficient operation of power systems, with direct implications for operational costs, energy management, and system maintenance. Due to the availability of extensive high-resolution consumption data from smart meters, data-driven methods have been used for short-term and long-term forecasting. However, their comparative performance on real-world smart meter data is still not well studied. In this paper, we present an empirical benchmark of nine modern deep learning models for time-series forecasting, including linear, MLP-based, convolutional, and Transformer architectures. We evaluate these models on two publicly available smart meter datasets. Our analysis focuses on three factors that strongly affect forecasting performance: the length of historical input, the prediction horizon, and the choice of model architecture. We show that extending the historical context improves accuracy, but only up to a saturation point, after which additional input provides limited benefit. In contrast, accuracy decreases as the prediction horizon increases. We also investigate the trade-off between prediction accuracy and computational complexity, and assess the statistical significance and practical magnitude of performance differences across models. Our results show that deep learning models consistently outperform classical baselines, while lightweight architectures achieve relatively similar performance at significantly lower computational cost. Additionally, architectural differences only become meaningful at longer forecasting horizons and on more heterogeneous datasets. Finally, a subgroup analysis across geodemographic and household categories shows that model choice has limited impact for most population segments.
