Back to Search Start Over

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

Authors :
Tian Shi
Fei Mei
Jixiang Lu
Jinjun Lu
Yi Pan
Cheng Zhou
Jianzhang Wu
Jianyong Zheng
Source :
Energies, Vol 12, Iss 22, p 4349 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

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.

Details

Language :
English
ISSN :
19961073
Volume :
12
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Energies
Publication Type :
Academic Journal
Accession number :
edsdoj.445bce4caf1240ac9c47aa38e313384f
Document Type :
article
Full Text :
https://doi.org/10.3390/en12224349