J. Tapia-García
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
J. Tapia-García has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- David Criado Ramón
Department of Computer Science and Artificial Intelligence, University of Granada, 18014 Granada, Spain
1 shared publication - Luis Gonzaga Baca Ruiz
Department of Software Engineering, University of Granada, 18014 Granada, Spain
1 shared publication - Manuel Pegalajar Cuellar
Department of Computer Science and Artificial Intelligence, University of Granada, 18014 Granada, Spain
1 shared publication
Latest Publications
- Open Access
- 3 July 2024
- Published by MDPI AG in Engineering Proceedings
- Vol. 68 (1) , 11
Big Data Techniques Applied to Forecast Photovoltaic Energy Demand in Spain
Renewable energies play an important role in our society’s development, addressing the challenges presented by climate change. Specifically, in countries like Spain, technologies such as solar energy assume a crucial significance, enabling the generation of clean energy. This study addresses the critical need to accurately predict photovoltaic (PV) energy demand in Spain. By using the data collected from the Spanish Electricity System, four models (Linear Regression, Random Forest, Recurrent Neural Network, and LightGBM) were implemented, with adaptations for Big Data. The LR model proved unsuitable, while the LGBM emerged as the most accurate and timely performer. The incorporation of Big Data adaptations amplifies the significance of our findings, highlighting the effectiveness of the LGBM in forecasting PV energy demand with both accuracy and efficiency.
- 3 July 2024
- Published by MDPI AG in Engineering Proceedings
- Vol. 68 (1) , 11
Open AccessBig Data Techniques Applied to Forecast Photovoltaic Energy Demand in Spain
Renewable energies play an important role in our society’s development, addressing the challenges presented by climate change. Specifically, in countries like Spain, technologies such as solar energy assume a crucial significance, enabling the generation of clean energy. This study addresses the critical need to accurately predict photovoltaic (PV) energy demand in Spain. By using the data collected from the Spanish Electricity System, four models (Linear Regression, Random Forest, Recurrent Neural Network, and LightGBM) were implemented, with adaptations for Big Data. The LR model proved unsuitable, while the LGBM emerged as the most accurate and timely performer. The incorporation of Big Data adaptations amplifies the significance of our findings, highlighting the effectiveness of the LGBM in forecasting PV energy demand with both accuracy and efficiency.