Our partners at the Athens University of Economics and Business published an article in the latest ACM SIGEnergy Energy Informatics Review issue.

The researchers propose a single-model RNN-based deep learning architecture named Deep4Ener, for consumer-level energy demand forecasting, trained on multiple users and capable of making predictions for unseen consumers with scarce historical data that were not included in the training phase.

Click on the link to learn more about Deep4Ener and how to make accurate forecasts for new consumers with limited historical data.