Research Article
An Expanded Study of the Application of Deep Learning Models in Energy Consumption Prediction
@INPROCEEDINGS{10.1007/978-3-031-22324-2_12, author={Leonardo Santos Amaral and Gustavo Medeiros de Ara\^{u}jo and Ricardo Moraes and Paula Monteiro de Oliveira Villela}, title={An Expanded Study of the Application of Deep Learning Models in Energy Consumption Prediction}, proceedings={Data and Information in Online Environments. Third EAI International Conference, DIONE 2022, Virtual Event, July 28-29, 2022, Proceedings}, proceedings_a={DIONE}, year={2022}, month={12}, keywords={Forecast Energy Demand Deep learning}, doi={10.1007/978-3-031-22324-2_12} }- Leonardo Santos Amaral
Gustavo Medeiros de Araújo
Ricardo Moraes
Paula Monteiro de Oliveira Villela
Year: 2022
An Expanded Study of the Application of Deep Learning Models in Energy Consumption Prediction
DIONE
Springer
DOI: 10.1007/978-3-031-22324-2_12
Abstract
The time series of electrical loads are complex, influenced by multiple variables (endogenous and exogenous), display non-linear behavior and have multiple seasonality with daily, weekly and annual cycles. This paper addresses the main aspects of demand forecast modeling from time series and applies machine learning techniques for this type of problem. The results indicate that through an amplified model including the selection of variables, seasonality representation technique selection, appropriate choice of model for database (deep or shallow) and its calibration, it’s possible to archive better results with an acceptable computational cost. In the conclusion, suggestions for the continuity of the study are presented.
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