Regional Wind Energy Resource Forecasting Based on SVD and Support Vector Machine

Hai Zhou; Jie Qiong He; Zhi Bao Chen

doi:10.4028/www.scientific.net/AMR.1070-1072.247

Paper Titles

Doubly Fed of Induction Generators Wind Turbine Frequency Control Based on Power Curve of Inertia Control and Pitch Angle Control
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Generator-Side Converter Control Strategies of Synchronous Wind Turbines Based on the MPPT
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Optimization of Large Scale Energy Storage System for Relaxing Wind Power Centralized Transmission Bottlenecks
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Regional Wind Energy Resource Forecasting Based on SVD and Support Vector Machine
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Research of Bidirectional Power Flow Algorithm of Micro-Grid Based on the Improved Zbus Method
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Research on Short-Circuit Current of Wind Power System and its Impact on Relay Protection
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Sharing of Unbalanced Loads in an Isolated Distributed Generation System with Droop Controlled Doubly Fed Induction Generator
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1070-1072Regional Wind Energy Resource Forecasting Based on...

Regional Wind Energy Resource Forecasting Based on SVD and Support Vector Machine

Abstract:

The aim of this paper is to forecast short term variation of the regional wind energy resource based on the data captured from several wind measuring stations. Firstly, the main spatial patterns are extracted by SVD (singular value decomposition) method, and then the time coefficient series corresponding to principal spatial patterns are processed and forecasted by SVM (support vector machine). Furthermore, according to the SVD method, the new forecasted time coefficient series are used to inversely calculate the wind speed in the future. Finally, the validity and performance of this forecasting method is tested in case study.

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Periodical:

Advanced Materials Research (Volumes 1070-1072)

Pages:

247-252

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1070-1072.247

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Online since:

December 2014

Authors:

Hai Zhou, Jie Qiong He, Zhi Bao Chen*

Keywords:

Spatial Patterns, Support Vector Machine (SVM), SVD Method, Time Series, Wind Energy Resource Forecasting

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* - Corresponding Author

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