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