Online Short-term Forecasting of Photovoltaic Energy Production

被引：0

作者：

Rashkovska, Aleksandra ^{[1
]}

Novljan, Jost ^{[1
]}

Smolnikar, Miha ^{[1
]}

Mohorcic, Mihael ^{[1
]}

Fortuna, Carolina ^{[1
]}

机构：

[1] Jozef Stefan Inst, Dept Commun Syst, Jamova Cesta 39, Ljubljana, Slovenia

来源：

2015 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT) | 2015年

关键词：

Forecasting; Neural networks; Photovoltaic systems; Microgrids;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Short-term forecasting of the energy production is one of the key issues in smart homes that tend to achieve efficient balance among the energy production, storage and consumption. In this paper, we first perform an analysis of the features to be used by the most promising short-term forecast model: artificial neural networks. We determine the best performing offline model and then propose an online model that is very close to the offline model in terms of prediction accuracy. The evaluation is performed on a real world data and the resulting system is part of a proof-of-concept application for microgrid management.

引用

页数：5

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