莫代利卡
光伏系统
信息物理系统
背景(考古学)
计算机科学
数据建模
物理系统
人工智能
机器学习
模拟
工程类
数据库
电气工程
生物
操作系统
物理
古生物学
量子力学
作者
Federico Delussu,Davide Manzione,Rosa Meo,Gabriele Ottino,Mark Asare
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-06-01
卷期号:18 (6): 4018-4028
被引量:25
标识
DOI:10.1109/tii.2021.3108688
摘要
We present two approaches for digital twinning in the context of the forecast of power production by photovoltaic panels. We employ two digital models that are complementary: the first one is a cyber-physical system, simulating the physical properties of a photovoltaic panel, built by the open- source object-oriented modeling language Modelica. The second model is data-driven, obtained by the application of machine learning techniques on the data collected in an installation of the equipment. Both approaches make use of data from the weather forecast of each day. We compare the results of the two approaches. Finally, we integrate them in more sophisticated hybrid systems that get the benefits of both.
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