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Comparative Analysis of Hybrid Deep Learning Frameworks for Energy Forecasting

Authors:

Olamide Jogunola,

Abimbola Susan Ajagun,

Bamidele Adebisi,

Abiodun Musa Aibinu,

John Adedapo OjoAuthors Info & Claims

ICFNDS '21: Proceedings of the 5th International Conference on Future Networks and Distributed Systems

Pages 214 - 219

https://doi.org/10.1145/3508072.3508105

Published: 13 April 2022 Publication History

Abstract

As energy forecasting is paramount to efficient grid planning, this work presents a comparative analysis of different hybrid deep learning frameworks for energy forecasting in applications such as energy consumption and trading. Specifically, we developed hybrid architectures comprising of Convolutional Neural Network (CNN), an Autoencoder (AE), Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM). We use the individual household electric power consumption dataset by University of California, Irvine to evaluate the proposed frameworks. We evaluated and compared the result of these frameworks using several error metrics. The results show an average MSE of ∼ 0.01 across all developed frameworks. In addition, the CNN-LSTM framework performed the least with a 20% and 10% higher RMSE and MAE to other frameworks respectively, while CNN-BiLSTM achieved the least computation time.

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Cited By

Hanif MSiddique MSi JNaveed MLiu XMi J(2024)Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied TerrainsAdvanced Theory and Simulations10.1002/adts.2023012897:7Online publication date: 30-Apr-2024
https://doi.org/10.1002/adts.202301289
da Silva DMeneses A(2023)Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption predictionEnergy Reports10.1016/j.egyr.2023.09.17510(3315-3334)Online publication date: Nov-2023
https://doi.org/10.1016/j.egyr.2023.09.175

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cover image ACM Other conferences

ICFNDS '21: Proceedings of the 5th International Conference on Future Networks and Distributed Systems

December 2021

847 pages

ISBN:9781450387347

DOI:10.1145/3508072

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2022

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The Department for Business, Energy and Industrial Strategy

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ICFNDS 2021

ICFNDS 2021: The 5th International Conference on Future Networks & Distributed Systems

December 15 - 16, 2021

Dubai, United Arab Emirates

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Cited By

Hanif MSiddique MSi JNaveed MLiu XMi J(2024)Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied TerrainsAdvanced Theory and Simulations10.1002/adts.2023012897:7Online publication date: 30-Apr-2024
https://doi.org/10.1002/adts.202301289
da Silva DMeneses A(2023)Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption predictionEnergy Reports10.1016/j.egyr.2023.09.17510(3315-3334)Online publication date: Nov-2023
https://doi.org/10.1016/j.egyr.2023.09.175

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