Multi-Level Fusion of Multi-Source Information Based Deep Learning and Ensemble Deep Learning Models

The emergence of combining different Deep Learning architectures in the Artificial Intelligence research field to perform a specific task offers new perspectives and at times, performance enhancement. In this paper, multiple levels of fusion based on single Deep Learning and Ensemble Deep Learning models is introduced while tackling the national electrical energy forecasting task of Algeria on a short-term basis using the multi-sourced data related to load demand and meteorological factors, provided by the System Operator of Algerian National Electricity and Gas Company (SONELGAZ). The different model architectures are based on Stacked Denoising Auto-encoder and One Dimensional Convolutional Neural Network. The different approaches' efficiency is tested by comparing the different empirical results based on the Mean Absolute Percentage Error. The obtained results not only underline the efficiency and precision of deep ANNs architectures but also show the impact of the Ensemble Deep Learning method when incorporated with the right level of information fusion.