Hybrid Model Analysis and Validation for PV energy production forecasting

被引：0
|
作者：
Gandelli, A. [1 ]
Grimaccia, F. [1 ]
Leva, S. [1 ]
Mussetta, M. [1 ]
Ogliari, E. [1 ]
机构：
[1] Politecn Milan, Dept Energy, Via Lambruschini 34, I-20133 Milan, Italy
来源：
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词：
NEURAL-NETWORKS; SYSTEMS; IRRADIANCE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a forecasting method for the Next Day's energy production forecast is proposed with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial Neural Network) based on Artificial Neural Network (ANN) and basic Physical constraints of the PV plant, is presented and compared with an ANN standard method. Furthermore, the accuracy of the two methods have been studied in order to better understand the intrinsic error committed by the PHANN, reporting some numerical results. This computing-based hybrid approach is proposed for PV energy forecasting in view of optimal usage and management of RES in future smart grid applications.
引用
收藏
页码：1957 / 1962
页数：6
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