CUED Publications database

Random vector functional link neural network for short-term wind power ramp forecasting

Ren, Y and Suganthan, PN and Srikanth, N and Amaratunga, G (2015) Random vector functional link neural network for short-term wind power ramp forecasting. In: UNSPECIFIED pp. 77-84..

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Abstract

Wind is a clean and renewable energy source with huge potential in power generation. However, due to the intermittent nature of the wind, the power generated by wind farms is uctuating and often has large ramps, which are harmful to the power grid. This paper presents algorithms to forecast the ramps in the wind power generation. The importance and challenges of accurate wind power ramp forecasting are addressed. Wind power ramp and power ramp rate are defined in this paper. A random vector functional link neural network (RVFLNN) is employed to forecast the wind power ramp and the ramp rate. The RVFLNN applied to wind power forecasting based on regression has comparable performance as the benchmark methods: Artificial neural network (ANN), random forests (RF) and support vector machine (SVM) but RVFLNN with classification approach has better performance than the other three benchmark methods. The computation time of training and testing is also in favor of RVFLNN. Possible future research directions are also identified.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div B > Electronics, Power & Energy Conversion
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:43
Last Modified: 03 Aug 2017 03:13
DOI: