利用多层次结构提高风能预测精度

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-30 DOI:10.1016/j.segan.2024.101517
Lucas English , Mahdi Abolghasemi
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引用次数: 0

摘要

可再生能源发电对全球去碳化至关重要。由于风能发电的固有不确定性取决于天气条件,因此对可再生能源,特别是风能进行预测具有挑战性。通过调和分层预测的最新进展表明,短期风能预测的质量显著提高。我们利用风电场中涡轮机的横截面和时间层次结构,建立跨时间层次结构,进一步研究综合横截面和时间维度如何为风电场的预测准确性增值。我们发现,在多个时间集合上,跨时间调节优于单个截面调节。此外,基于机器学习的跨时空协调预测在较粗的时间粒度上表现出较高的准确性,这可能会鼓励短期风力预测的采用。从经验上讲,我们为决策者提供了预测不同预测范围和水平的高频风力数据的最佳方法。
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Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.

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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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