Wind Power Prediction Using KLMS Algorithm

This study uses the kernalised version of Least Mean Square (LMS) algorithm which offers a technique of taking sample-by-sample update for reproducing kernel Hilbert spaces (RKHS), named as KLMS. This study uses online learning as it works in a dynamic mode where dependencies of variables change with time. Generally all statistical models build a model with historical data and make future predictions on the basis of developed model which is time-invariant. Also for a non-stationary process, it is not possible to capture the characteristics of relationship between input and output through a single curve. This paper presents an online method that builds a curve which evolves over the time. After the proposed approach is applied to the real world wind power system, the non-stationary behaviour of wind has been observed. The results of the simulation shows that the proposed technique can dynamically trace the time-varying nature of wind power system.