Neural Forecasting of the Day-Ahead Hourly Power Curve of a Photovoltaic Plant

被引：0

作者：

Ogliari, Emanuele ^{[1
]}

Gandelli, Alessandro ^{[1
]}

Grimaccia, Francesco ^{[1
]}

Leva, Sonia ^{[1
]}

Mussetta, Marco ^{[1
]}

机构：

[1] Politecn Milan, Dipartimento Energia, Via La Masa 34, I-20156 Milan, Italy

来源：

2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2016年

关键词：

MODEL;

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 we propose a study to identify the best ANN configuration in terms of number of neurons, number of layers, training-set size, in order to perform the day-ahead energy production forecast for a Photo-Voltaic (PV) plant. This set up is applied to a novel hybrid method (PHANN Physic Hybrid Artificial Neural Network) in order to enhance the energy dayahead forecast combining both the deterministic Clear Sky Solar Radiation Algorithm (CSRM) and the stochastic ANN method. This hybrid method has been tested on different PV plants in Italy. Finally, different weather conditions have been taken into account to test the robustness of the algorithm as well as the effects of particular events on the forecasting output.

引用

页码：654 / 659

页数：6

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