改进N-BEATS模型，提升中长期电力需求预测精度与稳定性
Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting
- 设计融合MAPE与归一化MSE的新型损失函数，平衡误差控制
- 引入去标准化组件优化块结构，提升多国数据统一处理效率
- 在35个欧洲国家月度用电数据上表现最优，误差波动最小
本文提出一种增强版N-BEATS模型N-BEATS*，用于改进中长期电力负荷预测（MTLF）。在原N-BEATS架构优势基础上，N-BEATS*引入两项关键改进：(1) 提出结合基于MAPE的分位数损失与归一化均方误差的新损失函数，实现对L1与L2损失项的更均衡捕捉；(2) 修改块结构，通过引入去标准化组件，协调不同时间序列的处理流程，使预测任务更高效、更简洁。在涵盖35个欧洲国家的真实月度用电数据集上评估显示，相比原模型及其他经典统计、机器学习与混合模型，N-BEATS*在所有指标上表现更优，取得最低的MAPE与RMSE，同时预测误差分布最为集中。
原文摘要 · Abstract (English)
This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture, which excels in handling complex time series data without requiring preprocessing or domain-specific knowledge, N-BEATS* introduces two key modifications. (1) A novel loss function -- combining pinball loss based on MAPE with normalized MSE, the new loss function allows for a more balanced approach by capturing both L1 and L2 loss terms. (2) A modified block architecture -- the internal structure of the N-BEATS blocks is adjusted by introducing a destandardization component to harmonize the processing of different time series, leading to more efficient and less complex forecasting tasks. Evaluated on real-world monthly electricity consumption data from 35 European countries, N-BEATS* demonstrates superior performance compared to its predecessor and other established forecasting methods, including statistical, machine learning, and hybrid models. N-BEATS* achieves the lowest MAPE and RMSE, while also exhibiting the lowest dispersion in forecast errors.
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