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Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review

Author

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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)

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.

Suggested Citation

  • Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:1:p:13-:d:1316869
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    References listed on IDEAS

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    Cited by:

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    2. Omar Farhan Al-Hardanee & Hüseyin Demirel, 2024. "Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection," Energies, MDPI, vol. 17(22), pages 1-23, November.
    3. Jun Li & Xingzhao Zhang & Qingsong Hu & Fuxi Zhang & Oleg Gaidai & Leilei Chen, 2024. "Data Augmentation Technique Based on Improved Time-Series Generative Adversarial Networks for Power Load Forecasting in Recirculating Aquaculture Systems," Sustainability, MDPI, vol. 16(23), pages 1-17, December.
    4. Yukta Mehta & Vincent Lo & Vijen Mehta & Kunal Agrawal & Charan Teja Madabathula & Eugene Chang & Jerry Gao, 2025. "Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning," Energies, MDPI, vol. 18(6), pages 1-29, March.

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