  Integration of renewable energy sources and emerging loads like electric
vehicles to smart grids brings more uncertainty to the distribution system
management. Demand Side Management (DSM) is one of the approaches to reduce the
uncertainty. Some applications like Nonintrusive Load Monitoring (NILM) can
support DSM, however they require accurate forecasting on high resolution data.
This is challenging when it comes to single loads like one residential
household due to its high volatility. In this paper, we review some of the
existing Deep Learning-based methods and present our solution using Time
Pooling Deep Recurrent Neural Network. The proposed method augments data using
time pooling strategy and can overcome overfitting problems and model
uncertainties of data more efficiently. Simulation and implementation results
show that our method outperforms the existing algorithms in terms of RMSE and
MAE metrics.

Machine Learning;Computer Vision and Pattern Recognition