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Energies 2018, 11(11), 3089; https://doi.org/10.3390/en11113089

Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

1
Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China
2
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
3
State Grid Zhejiang Electric Power Co., Ltd, Hangzhou 310007, China
*
Author to whom correspondence should be addressed.
Received: 30 September 2018 / Revised: 31 October 2018 / Accepted: 7 November 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Intelligent Control in Energy Systems)
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Abstract

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage. View Full-Text
Keywords: electric power consumption; multi-step forecasting; long short term memory; convolutional neural network electric power consumption; multi-step forecasting; long short term memory; convolutional neural network
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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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Yan, K.; Wang, X.; Du, Y.; Jin, N.; Huang, H.; Zhou, H. Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy. Energies 2018, 11, 3089.

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