Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

Filipe D. Campos, Tiago C. Sousa and Ramiro S. Barbosa ()
Additional contact information
Filipe D. Campos: Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal
Tiago C. Sousa: Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal
Ramiro S. Barbosa: Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal

Energies, 2024, vol. 17, issue 11, 1-19

Abstract: 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 R 2 , for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

Keywords: short-term forecasting; LSTM; solar energy production; ANN; CNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2582/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2582/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:11:p:2582-:d:1402892

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().