Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network
This work addresses the need for accurate forecasting to support Demand Side Management in smart grids, but it is incremental as it builds on existing deep learning methods with a specific enhancement.
The paper tackles short-term load forecasting for single residential households, which is challenging due to high volatility, by proposing a Time Pooling Deep Recurrent Neural Network that augments data to overcome overfitting and model uncertainties, resulting in improved performance over existing algorithms in terms of RMSE and MAE metrics.
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.