WPBench: A Comprehensive Benchmark for Wind Power Forecasting

2026-09-21

AI Summary

为解决风电预测在系统评估方面的不足，本文提出WPBench，一个综合性的基准测试平台，通过整合26个公开数据集和19种代表性模型来全面、公平地评估风电预测方法。

Abstract

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.

Problem

• wind power forecasting
• benchmark
• evaluation metrics
• spatial structure
• model families

Innovation

• Wind Power Forecasting
• Benchmark
• Comprehensive Datasets
• Model Comparison
• Structure-Aware Diagnostics