Forecasting high‐frequency spatio‐temporal wind power with dimensionally reduced echo state networks
Forecasting high‐frequency spatio‐temporal wind power with dimensionally reduced echo state networks
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DOI：
10.1111/rssc.12540
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Huang Huang-Huang;S. Castruccio;M. Genton
中科院分区：
文献类型：
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作者：
Huang Huang-Huang;S. Castruccio;M. Genton
快速、准确的每小时风速和功率预测对于量化和规划电网的能源预算至关重要。考虑到风的湍流和高度的非线性动力学，高分辨率的风模化带来了相当大的挑战。在发展中国家，目前正在建设或考虑建设大面积的风力发电场，考虑到也需要对空间上的风进行建模，这一点甚至更具挑战性。在这项工作中，我们提出了一种机器学习方法来模拟沙特阿拉伯的非线性每小时风动力学，并通过特定领域的节点选择来降低空间维度。我们的结果显示，对于先前工作强调风能丰富的地区，我们的方法使提前2小时的预测电力相对于风能行业的运营标准提高了11%，在沙特阿拉伯当前的市场价格下，一年节省了近100万美元。
Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid. Modelling wind at a high resolution brings forth considerable challenges given its turbulent and highly nonlinear dynamics. In developing countries, where wind farms over a large domain are currently under construction or consideration, this is even more challenging given the necessity of modelling wind over space as well. In this work, we propose a machine learning approach to model the nonlinear hourly wind dynamics in Saudi Arabia with a domain‐specific choice of knots to reduce spatial dimensionality. Our results show that for locations highlighted as wind abundant by a previous work, our approach results in an 11% improvement in the 2‐h‐ahead forecasted power against operational standards in the wind energy sector, yielding a saving of nearly one million US dollars over a year under current market prices in Saudi Arabia.