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Energies 2018, 11(12), 3433; https://doi.org/10.3390/en11123433

Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
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Received: 31 October 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 7 December 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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Abstract

Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition. View Full-Text
Keywords: deep learning; empirical mode decomposition (EMD); long short-term memory (LSTM); load forecasting; neural networks; variational mode decomposition (VMD); weekly decomposition deep learning; empirical mode decomposition (EMD); long short-term memory (LSTM); load forecasting; neural networks; variational mode decomposition (VMD); weekly decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kim, S.H.; Lee, G.; Kwon, G.-Y.; Kim, D.-I.; Shin, Y.-J. Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting. Energies 2018, 11, 3433.

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