Modeling and Prediction of the Energy Intensity Index by Provinces of Ecuador Using Neural Networks and Cluster Analysis with Machine Learning Algorithms

Using a comparative experimental design, four neural network architectures—feedforward, LSTM, sequential feedforward, and robust LSTM—are evaluated to predict the provincial energy intensity index, based on panel data from 2018 to 2023 and key variables related to energy consumption, economic activi...

• Autor principal: Rodríguez, Lester (author)
• Altres autors: Litardo Unda, Julio Goivanni (author)
• Format: article
• Idioma: spa
• Publicat: 2025
• Matèries: Energy Intensity; Artificial Neural Networks; Machine Learning; K-means; DBSCAN; Intensidad energética; Redes neuronales artificiales; Aprendizaje automático
• Accés en línia: https://estudioseconomicos.bce.fin.ec/index.php/RevistaCE/article/view/502