Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network
Joint Authors
Niu, Dongxiao
Wei, Yanan
Chen, Yanchao
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-09
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent.
Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system.
The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power.
According to the proposed scene simulation knowledge mining (SSKM) technique, the influencing factors are clustered and fused into prediction model.
Combining adaptive algorithm with neural network, adaptive neural network prediction model is established.
Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.
American Psychological Association (APA)
Niu, Dongxiao& Wei, Yanan& Chen, Yanchao. 2013. Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1031767
Modern Language Association (MLA)
Niu, Dongxiao…[et al.]. Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network. Mathematical Problems in Engineering No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-1031767
American Medical Association (AMA)
Niu, Dongxiao& Wei, Yanan& Chen, Yanchao. Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1031767
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031767