Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

Tian Shi, Fei Mei, Jixiang Lu, Jinjun Lu, Yi Pan, Cheng Zhou, Jianzhang Wu and Jianyong Zheng
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Tian Shi: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Fei Mei: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Jixiang Lu: State Key Laboratory of Smart Grid Protection and Control, NARI Group Corporation, Nanjing 211000, China
Jinjun Lu: State Key Laboratory of Smart Grid Protection and Control, NARI Group Corporation, Nanjing 211000, China
Yi Pan: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Cheng Zhou: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Jianzhang Wu: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Jianyong Zheng: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2019, vol. 12, issue 22, 1-17

Abstract: 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.

Keywords: Load forecasting; VSTLF; bus load forecasting; DBN; PSR; deep learning (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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