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.

翻译：将可再生能源和新兴载荷(如电动车辆整合到智能电网)给分配系统管理带来了更多的不确定性。需求方管理(DSM)是减少不确定性的方法之一。一些应用程序,如无侵扰性负载监测(NILM),可以支持DSM,但是它们需要对高分辨率数据进行准确的预测。在像一个住户那样的单个载荷因其高度波动而具有挑战性。在本文中,我们审查一些现有的深层学习方法,并利用时间共享深度常识神经网络提出我们的解决方案。拟议方法利用时间共享战略来增加数据,并能够更有效地克服数据过于适合的问题和模型不确定性。模拟和实施结果显示,我们的方法在RMSE和MAE标准方面超过了现有的算法。