TY - JOUR
T1 - Adaptive Forecasting in Energy Consumption
T2 - A Bibliometric Analysis and Review
AU - Jaramillo, Manuel
AU - Pavón, Wilson
AU - Jaramillo, Lisbeth
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - adaptive energy forecasting
KW - bibliometric analysis
KW - LSTM-based energy forecasting
KW - optimization in adaptive forecasting
KW - time series prediction
UR - http://www.scopus.com/inward/record.url?scp=85183416555&partnerID=8YFLogxK
U2 - 10.3390/data9010013
DO - 10.3390/data9010013
M3 - Review article
AN - SCOPUS:85183416555
SN - 2306-5729
VL - 9
JO - Data
JF - Data
IS - 1
M1 - 13
ER -