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Energy consumption prediction model with deep inception residual network inspiration and LSTM

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  • Salam, Abdulwahed
  • El Hibaoui, Abdelaaziz

Abstract

Predicting electricity consumption is not an easy task depending on many factors that affect energy consumption. Therefore, electricity utilities and governments are always searching for intelligent models to improve the accuracy of prediction and recently, deep learning becomes the most used field in prediction. In this paper, we introduce a deep learning model based on deep feedforward neural networks and Long Short-Term Memory. The deep feedforward neural networks architecture was inspired by the Inception Residual Network v2, which achieved the highest accuracy in image classification. We compared our proposed model to other recent deep learning models in two different datasets: dataset from the Distribution Network Station of Tetouan city in Morocco and dataset from the North American Utility. The proposed model achieved the smallest error of Root Mean Square Error comparing to its counterparts.

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  • Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
    • Handle: RePEc:eee:matcom:v:190:y:2021:i:c:p:97-109
    DOI: 10.1016/j.matcom.2021.05.006
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    2. Jianliang He & Yuxin Sun & Chen Yin & Yan He & Yulin Wang, 2023. "Cross-domain adaptation network based on attention mechanism for tool wear prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3365-3387, December.
    3. Siti Aisyah & Arionmaro Asi Simaremare & Didit Adytia & Indra A. Aditya & Andry Alamsyah, 2022. "Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia," Energies, MDPI, vol. 15(10), pages 1-17, May.
    4. Nebojsa Bacanin & Catalin Stoean & Miodrag Zivkovic & Miomir Rakic & Roma Strulak-Wójcikiewicz & Ruxandra Stoean, 2023. "On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting," Energies, MDPI, vol. 16(3), pages 1-21, February.

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