NDLI: Improved prediction method of PV output power based on optimised chaotic phase space reconstruction

Content Provider	IET Digital Library
Author	Wang, Yufei Fu, Yuchao Xue, Hua
Abstract	With a large number of photovoltaic (PV) generation systems connected to power grid, accurate forecasting becomes important, the results can be used to alleviate their impacts on the grid effectively. However, most of existing methods strongly rely on the numerical weather prediction (NWP), accuracy of them under highly volatile weather conditions is poor. In this study, without using meteorological data, an improved prediction method based on optimised chaotic phase space reconstruction is presented. Firstly, chaos theory is introduced to analyse the evolution law of PV output power. Then, in order to decrease the delay effect of original chaotic attractor and the negative effect of each phase space power sequence's fluctuation on forecasting accuracy, the algorithm of ensemble empirical mode decomposition (EEMD) is introduced to perform further analysis, so as to raise the regularity of chaotic attractors and extract the partial fluctuation features. Finally, based on optimized chaotic attractor and genetic algorithm-back propagation (GA-BP) neural network, the authors build a combined prediction model and apply it into actual measurement data to verify its validation. Numerical results show that by carrying out the optimised chaotic phase space reconstruction, proposed prediction approach achieves better accuracy than the chaos-GA-BP, EEMD-GA-BP and NWP-GA-BP methods.
Starting Page	1831
Ending Page	1840
Page Count	10
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 11, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.0809
Journal	IET Renewable Power Generation
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Chaos Chaotic Attractor Chaotic Phase Space Reconstruction Chaotic Theory EEMD Ensemble Empirical Mode Decomposition GA-BP Neural Network Genetic Algorithm Genetic Algorithm-back Propagation Geophysics Computing Neural Computing Technique Neural Nets Numerical Weather Prediction NWP-GA-BP Method Optimisation Technique Phase Space Power Sequence Photovoltaic Generation System Photovoltaic Power System Power Grid PV Output Power Time Series Weather Analysis And Prediction Weather Forecasting
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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