Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

被引：3

作者：

Campos, Filipe D. ^{[1
]}

Sousa, Tiago C. ^{[1
]}

Barbosa, Ramiro S. ^{[1
,2
]}

机构：

[1] Polytech Porto ISEP IPP, Dept Elect Engn, Inst Engn, P-4249015 Porto, Portugal

[2] ISEP IPP, GECAD Res Grp Intelligent Engn & Comp Adv Innovat, P-4249015 Porto, Portugal

来源：

ENERGIES | 2024年 / 17卷 / 11期

关键词：

short-term forecasting; LSTM; solar energy production; ANN; CNN;

D O I：

10.3390/en17112582

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：

In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R2, for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

引用

页数：19

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