A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output
A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output
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DOI：
10.3390/en8021138
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发表时间：
2015-02
期刊：
影响因子：
3.2
通讯作者：
A. Dolara;F. Grimaccia;S. Leva;M. Mussetta;E. Ogliari
中科院分区：
文献类型：
--
作者：
A. Dolara;F. Grimaccia;S. Leva;M. Mussetta;E. Ogliari
这项工作的主要目的是对光伏发电厂的发电活动进行提前预测评估。预测方法可以在解决智能电网中可再生能源（RES）集成相关问题中发挥重要作用。在这里，一个新的混合方法称为物理混合人工神经网络（PHANN）的人工神经网络（ANN）和光伏电站晴空曲线的基础上，提出并与标准的人工神经网络方法进行比较。此外，这两种方法的准确性进行了分析，以更好地了解所造成的PHANN的内在误差，并评估其在能源预测应用的潜力。
The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.