WPBench：风力发电预测综合基准
WPBench: A Comprehensive Benchmark for Wind Power Forecasting
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中文总结 AI 辅助
针对现有风电预测基准在场景覆盖、模型家族、评估指标和结构诊断上的不足，提出WPBench综合基准，整合26个数据集和19个模型，提供统一评估与多维度诊断，促进系统性模型比较。
中文摘要 AI 辅助
准确、可靠且可部署的风力发电预测对于电力系统调度、可再生能源并网和电力市场运营至关重要。该领域的进展取决于对预测方法进行实证和全面基准测试的能力。然而，现有基准在四个关键方面不足以支持系统性评估：1）在风机规模、变量组成和空间结构方面对风电场景的覆盖有限；2）预测模型家族覆盖不完整；3）评估指标与风电需求不一致；4）除个体时间模式外，缺乏结构感知的诊断。为解决这些局限性，我们提出了WPBench，一个全面、公平且可扩展的风力发电预测基准。WPBench整合了26个按风机规模和变量组成组织的公开数据集，涵盖单风机、多风机、单变量和多变量设置。在统一的处理、训练和评估协议下，它基准测试了19个代表性模型，涵盖传统方法、深度时间模型、时空模型和基础模型。除逐点误差外，WPBench还评估预测曲线保真度和计算效率，并从时间、变量依赖和空间依赖角度提供结构感知的诊断。这些能力共同实现了跨多样风电场景的系统性模型比较，并为未来研究提供了可复用的平台。
英文摘要
Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market operations. Progress in this field hinges on the ability to empirically and comprehensively benchmark forecasting methods. Yet existing benchmarks fall short of supporting systematic evaluation in four key aspects: 1) limited coverage of wind power scenarios across turbine scale, variable composition, and spatial structure; 2) incomplete coverage of forecasting model families; 3) evaluation metrics misaligned with wind power requirements; and 4) limited structure-aware diagnostics beyond individual temporal patterns. To address these limitations, we propose WPBench, a comprehensive, fair, and extensible benchmark for wind power forecasting. WPBench integrates 26 public datasets organized by turbine scale and variable composition, spanning single-turbine, multi-turbine, univariate, and multivariate settings. Under unified processing, training, and evaluation protocols, it benchmarks 19 representative models covering traditional methods, deep temporal models, spatio-temporal models, and foundation models. Beyond point-wise errors, WPBench assesses forecast-curve fidelity and computational efficiency, and delivers structure-aware diagnostics across temporal, variable-dependency, and spatial-dependency perspectives. Together, these capabilities enable systematic model comparison across diverse wind scenarios and provide a reusable platform for future research.
发表机构
- East China Normal University（华东师范大学）
- Zhejiang University（浙江大学）
- Hangzhou Hikvision Digital Technology Co., Ltd.（杭州海康威视数字技术股份有限公司）
机构由 AI 辅助整理，请以论文原文为准。
