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An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

1
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
2
School of Electrical Engineering, Southeast University, Nanjing 210096, China
3
State Key Laboratory of Smart Grid Protection and Control, NARI Group Corporation, Nanjing 211000, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(7), 1487; https://doi.org/10.3390/app9071487
Received: 28 February 2019 / Revised: 24 March 2019 / Accepted: 2 April 2019 / Published: 9 April 2019
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Abstract

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions. View Full-Text
Keywords: net load forecasting; phase space reconstruction; deep neural network net load forecasting; phase space reconstruction; deep neural network
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Mei, F.; Wu, Q.; Shi, T.; Lu, J.; Pan, Y.; Zheng, J. An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network. Appl. Sci. 2019, 9, 1487.

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