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.
Original language | English |
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Article number | 13 |
Journal | Data |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2024 |
Bibliographical note
Publisher Copyright:© 2024 by the authors.
Keywords
- adaptive energy forecasting
- bibliometric analysis
- LSTM-based energy forecasting
- optimization in adaptive forecasting
- time series prediction