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Energies 2017, 10(1), 3; doi:10.3390/en10010003

Deep Neural Network Based Demand Side Short Term Load Forecasting

1
Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 121-742, Korea
2
Software Center, Korea Electric Power Corporation (KEPCO), 105 Munji Road, Yuseong-Gu, Daejeon 305-760, Korea
This paper is an extended version of our paper published in Proceedings of the 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia, 6–9 November 2016.
*
Author to whom correspondence should be addressed.
Academic Editor: José C. Riquelme
Received: 15 July 2016 / Revised: 25 November 2016 / Accepted: 16 December 2016 / Published: 22 December 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
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Abstract

In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN), a double seasonal Holt–Winters (DSHW) model and the autoregressive integrated moving average (ARIMA). The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW. View Full-Text
Keywords: short-term load forecasting; deep neural network; deep learning; rectified linear unit (ReLU); exponential smoothing; smart grid; restricted Boltzmann machine (RBM); pre-training short-term load forecasting; deep neural network; deep learning; rectified linear unit (ReLU); exponential smoothing; smart grid; restricted Boltzmann machine (RBM); pre-training
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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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Ryu, S.; Noh, J.; Kim, H. Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies 2017, 10, 3.

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