Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review

Manuel Jaramillo (), Wilson Pavón and Lisbeth Jaramillo
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Manuel Jaramillo: Smart Grid Research Group—GIREI, Electrical Engineering Deparment, Salesian Polytechnic University, Quito EC170702, Ecuador
Wilson Pavón: Smart Grid Research Group—GIREI, Electrical Engineering Deparment, Salesian Polytechnic University, Quito EC170702, Ecuador
Lisbeth Jaramillo: Medical School, Pontifical Catholic University of Ecuador, Quito EC200102, Ecuador

Data, 2024, vol. 9, issue 1, 1-23

Abstract: 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.

Keywords: bibliometric analysis; adaptive energy forecasting; time series prediction; LSTM-based energy forecasting; optimization in adaptive forecasting (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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