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

Seon Hyeog Kim, Gyul Lee, Gu-Young Kwon, Do-In Kim and Yong-June Shin
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Seon Hyeog Kim: Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
Gyul Lee: Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
Gu-Young Kwon: Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
Do-In Kim: Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
Yong-June Shin: Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea

Energies, 2018, vol. 11, issue 12, 1-17

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.

Keywords: deep learning; empirical mode decomposition (EMD); long short-term memory (LSTM); load forecasting; neural networks; variational mode decomposition (VMD); weekly decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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