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

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. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a deep learning model based on deep feedforward neural networks and Long Short-Term Memory for predicting electricity consumption. Through comparisons with other models on two datasets, it is proven that the proposed model achieves the smallest error in predicting electricity consumption.