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

Abdulwahed Salam and Abdelaaziz El Hibaoui

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 190, issue C, 97-109

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.

Keywords: Prediction; Machine learning; Deep learning; Power production and consumption (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:190:y:2021:i:c:p:97-109

DOI: 10.1016/j.matcom.2021.05.006

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