Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review

Manuel Jaramillo, Wilson Pavón, Lisbeth Jaramillo

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations


This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while also analyzing the evolution of research in this field through bibliometric analysis. The review highlights the key contributions and limitations of current models with an emphasis on the challenges of traditional methods. The analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption, but they also have higher computational demands and data requirements. This review aims to offer a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems.

Original languageEnglish
Article number13
Issue number1
StatePublished - Jan 2024

Bibliographical note

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© 2024 by the authors.


  • adaptive energy forecasting
  • bibliometric analysis
  • LSTM-based energy forecasting
  • optimization in adaptive forecasting
  • time series prediction


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