提高风电功率预测精度的方法

Research & Application of Raising Wind Power Prediction Accuracy

风电功率预测技术既是交叉应用学科,又是众多学科的共性基础性学科。目前国际最先进水平的风电预测误差甚至低于3%,我国还处于追赶期。从发展趋势来看,当前数据积累带来的精度提升效果日渐式微,数值天气预报短期内出现突破不可预期。风功率预测在未来一段时间内仍需要有限的天气预报水平下提高各环节的预报技巧,筛选和组合预测方法,争取误差学习曲线最末端2%~3%。若干实践表明,综合应用多项精细化预测技术,可显著提高原有水平较差的风场精度。这些技术包括优化数值天气预报参数化方案、提高功率转换环节精度与自适应能力、采用复合数据源的组合法、考虑空间相关性的非线性误差修正等。

Wind power prediction is an interdisciplinary application and also a mutual fundamental discipline. Global state-of-art of prediction error is even below 3%, whereas in China more efforts should be made to top out. According to present trend, benefits of accuracy improvement from accumulative data slow down, and incredible breakthrough of numeric weather prediction（NWP） is unexpected. For wind power prediction in near future, it is important to improve prediction skills in every step considering NWP limits, select and combine proper methods to reduce the final 2%-3% in error learning curve. Experiences show that accuracy of bad case could be improved remarkably by making comprehensive efforts, including optimizing NWP modeling parameterization, improving accuracy and self-adaptive capability of wind power output model, applying data from various sources, and nonlinear error correction considering spatial relativity.