Abstract: Energy demand continues to increase with no prospect of slowing down in the future. This increase is caused by several sociological and economical factors such as population growth, urbanization and technological developments. In view of this growth, it becomes crucial to predict energy consumption for a more accurate management and optimization. Nevertheless, consumption estimation is a complex task due to consumer behaviour fluctuation and weather alterations. Several efforts were proposed in the literature. Almost, all of them focused on improving the prediction model to increase the accuracy of the results. They use the LSTM (Long-Short Term Memory) model to reflect the temporal dependencies between historical data despite its spatial and temporal complexities. The main contribution in this paper is a novel and simple Convolutional Neural Network energy prediction model based on input data structure enhancement. The main idea is to adjust the structure of the input data instead of using a more complicated deep learning model for better performance. The proposed model was implemented, tested using real data and compared to existing ones. The obtained results showed that the proposed data structure has a great influence on the model performance measurement.

Imen Toumia and Ahlem Ben Hassine, “Impact of Input Data Structure on Convolutional Neural Network Energy Prediction Model” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131089

@article{Toumia2022,
title = {Impact of Input Data Structure on Convolutional Neural Network Energy Prediction Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131089},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131089},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Imen Toumia and Ahlem Ben Hassine}
}