Autors: Aravena-Cifuentes A. P., Nuñez-Gonzalez J. D., Elola A., Ivanova, M. S.
Title: Development of AI-Based Tools for Power Generation Prediction
Keywords: energy, prediction, regression, r-squared

Abstract: This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models.

References

    Issue

    Computation, vol. 11, issue 11, pp. 1-15, 2023, Switzerland, Multidisciplinary Digital Publishing Institute, ISSN: 20793197/DOI: 10.3390/computation11110232

    Copyright MDPI

    Цитирания (Citation/s):
    1. Aravena-Cifuentes, A.P., Porlan-Ferrando, L., Nuñez-Gonzalez, J.D., Graña, M., Brainstorming on Dataset Reduction from an Heuristic Bioinspired Green Computing Approach. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_41. - 2024 - в издания, индексирани в Scopus или Web of Science
    2. Aravena-Cifuentes, A.P., Nuñez-Gonzalez, J.D., Roldán, D.M., Altamiranda, J., Graña, M., Predicting Power Generation from Photovoltaic Energy System. In: Quintián, H., et al. The 19th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2024. SOCO 2024. Lecture Notes in Networks and Systems, vol 888, 2024. Springer, Cham. https://doi.org/10.1007/978-3-031-75013-7_20. - 2024 - в издания, индексирани в Scopus или Web of Science
    3. Al Mamun Rudro, R., Nahar, A., Al Sohan, M.F.A., SPXAI: Solar Power Generation with Explainable AI Technology, Energy Proceedings, vol. 51, 2025. ISSN: 20042965, DOI: 10.46855/energy-proceedings-11441. - 2025 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus