Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network

• URL: http://arxiv.org/abs/2109.12498v1
• Date: Sun, 26 Sep 2021 05:20:48 GMT
• Title: Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network
• Authors: Elahe Khoshbakhti Vaygan, Roozbeh Rajabi, Abouzar Estebsari
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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