Daily Prediction of PV Power Output Using Particulate Matter Parameter with Artificial Neural Networks

被引：1

作者：

Irmak, Erdal ^{[1
]}

Yesilbudak, Mehmet ^{[2
]}

Tasdemir, Oguz ^{[3
]}

机构：

[1] Gazi Univ, Fac Technol, Dept Elect & Elect Engn, Ankara, Turkiye

[2] Nevsehir Haci Bektas Veli Univ, Fac Engn & Architecture, Dept Elect & Elect Engn, Nevsehir, Turkiye

[3] Kirsehir Ahi Evran Univ, Vocat Coll Kaman, Dept Elect & Elect, Kirsehir, Turkiye

来源：

2023 11TH INTERNATIONAL CONFERENCE ON SMART GRID, ICSMARTGRID | 2023年

关键词：

PV power; daily prediction; artificial neural networks; PM10; parameter;

D O I：

10.1109/ICSMARTGRID58556.2023.10171103

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Renewable energy sources play a critical role in meeting the increasing energy demand. Among them, solar energy stands out with the advantages of being environmentally friendly and protecting the ecosystem. However, its variable structure requires predicting the energy to be produced, properly. In this study, the impact of PM10 parameter on the power output prediction of photovoltaic (PV) energy plants was analyzed in a detailed manner. By the developed prediction model based on artificial neural networks (ANNs), lower root mean squared error and mean absolute percentage error were achieved. As a result, PM10 parameter has seemed to be an efficient input for the daily PV power prediction.

引用

页数：4

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