Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting
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School of Electrical Engineering, Southeast University, Nanjing 210096, China
2
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
3
State Key Laboratory of Smart Grid Protection and Control, NARI Group Corporation, Nanjing 211000, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(22), 4349; https://doi.org/10.3390/en12224349
Received: 20 October 2019
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Revised: 12 November 2019
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Accepted: 13 November 2019
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Published: 15 November 2019
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.
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Keywords:
Load forecasting; VSTLF; bus load forecasting; DBN; PSR; deep learning
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MDPI and ACS Style
Shi, T.; Mei, F.; Lu, J.; Lu, J.; Pan, Y.; Zhou, C.; Wu, J.; Zheng, J. Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting. Energies 2019, 12, 4349. https://doi.org/10.3390/en12224349
AMA Style
Shi T, Mei F, Lu J, Lu J, Pan Y, Zhou C, Wu J, Zheng J. Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting. Energies. 2019; 12(22):4349. https://doi.org/10.3390/en12224349
Chicago/Turabian StyleShi, Tian, Fei Mei, Jixiang Lu, Jinjun Lu, Yi Pan, Cheng Zhou, Jianzhang Wu, and Jianyong Zheng. 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting" Energies 12, no. 22: 4349. https://doi.org/10.3390/en12224349
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