A Hierarchical Approach to Probabilistic Wind Power Forecasting

被引：0

作者：

Gilbert, Ciaran ^{[1
]}

Browell, Jethro ^{[1
]}

McMillan, David ^{[1
]}

机构：

[1] Univ Strathclyde, Glasgow, Lanark, Scotland

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS) | 2018年

基金：

英国工程与自然科学研究理事会;

关键词：

Wind power; probabilistic forecasting; hierarchical forecasting; forecasting; wind power integration; REGULARIZATION;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.

引用

页数：6

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