基于动态神经网络的光伏发电系统灰盒建模

Naji Al-Messabi, C. Goh, Yun Li
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引用次数: 2

摘要

光伏系统的建模和预测方法多种多样。这些方法可以分为两类,一类是明确的方程模型(白盒或清盒),另一类是启发式数据驱动的人工智能模型(黑盒)。这两个方向的建模带来了一些缺点。本文提出了一种利用聚焦时滞神经网络模型对不确定性进行建模,将清盒模型扩展为灰盒模型的新方法。灰盒或半确定模型显示出增强的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grey-Box Modeling for Photo-Voltaic Power Systems Using Dynamic Neural-Networks
There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities.
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