Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble
Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble
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DOI：
10.1016/j.renene.2017.02.052
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发表时间：
2017-08-01
期刊：
影响因子：
8.7
通讯作者：
Delle Monache, Luca
中科院分区：
文献类型：
--
作者：
Cervone, Guido;Clemente-Harding, Laura;Delle Monache, Luca
提出了一种基于人工神经网络（ANN）和模拟Ensemble（AnEn）的方法来生成72小时的确定性和概率预测的光伏（PV）发电厂所产生的功率，使用输入的数值天气预报模型和计算的天文变量。ANN和AnEn分别使用和组合生成位于意大利的三个太阳能发电厂的预测。使用模拟4450个光伏电站的合成数据测试所提出的解决方案的计算可扩展性。采用美国国家大气研究中心（NCAR）的黄石超级计算机对所提方案的并行实现进行了测试，测试范围从1个节点（32核）到4450个节点（141,140核）。结果表明，一个组合的AnEn + ANN解决方案产生最好的结果，所提出的解决方案是非常适合大规模计算。爱思唯尔有限公司出版
A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72 h deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plants located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4450 PV power stations. The National Center for Atmospheric Research (NCAR) Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from one node (32 cores) to 4450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation. Published by Elsevier Ltd.