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

Conference paper

Vaygan, E. K., Rajabi, R and Estebsari, A. (2021). Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network. 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584634
AuthorsVaygan, E. K., Rajabi, R and Estebsari, A.
TypeConference paper
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.

KeywordsLoad forecasting, Time pooling, Deep learning, Smart grids
Year2021
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584634
Web address (URL)https://ieeexplore.ieee.org/document/9584634
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Publication dates
Print03 Nov 2021
Publication process dates
AcceptedJul 2021
Deposited04 Nov 2021
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