Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation
Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation
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DOI：
10.1109/tste.2014.2359974
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发表时间：
2015
影响因子：
8.8
通讯作者：
Chen Yang;A. Thatte;Le Xie
中科院分区：
文献类型：
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作者：
Chen Yang;A. Thatte;Le Xie
随机光伏发电在电力系统中的渗透率越来越高，给系统运营商带来了巨大的挑战。为了确保电力系统的可靠运行，准确预测光伏发电量至关重要。在本文中，我们提出了一种新的多时间尺度数据驱动的预测模型，以提高短期光伏发电的准确性。该模型利用了相邻太阳站点之间的空间和时间相关性，并且与传统的持久性（PSS）模型相比具有更好的性能。使用加州和科罗拉多的PV站点的真实的数据集研究了计算成本和改进的预测质量之间的权衡。
The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses significant challenges to system operators. To ensure reliable operation of power systems, accurate forecasting of PV power production is essential. In this paper, we propose a novel multitime-scale data-driven forecast model to improve the accuracy of short-term PV power production. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared to the conventional persistence (PSS) model. The tradeoff between computation cost and improved forecast quality is studied using real datasets from PV sites in California and Colorado.