Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast

Abstract

Wind ramps introduce significant uncertainty into wind power generation. Reliable system operation, however, requires accurate detection and forecast of wind ramps, especially at high penetration levels. In this paper, to deal with the wind ramp dynamics, a support vector machine (SVM)-enhanced Markov model is developed for short-term wind power forecast, based on one key observation from the measurement data that wind ramps often occur with specific patterns. Specifically, using the historical data of the wind turbine power outputs recorded at an actual wind farm, data analytics-based finite-state Markov models are first developed to model the normal fluctuations of wind generation, while taking into account the diurnal nonstationarity and the seasonality of wind generation. Next, the forecast by the SVM is integrated cohesively into the finite-state Markov models. Based on the SVM-enhanced Markov model, both short-term distributional forecasts and point forecasts are then derived. Numerical test results, using real wind generation data traces, demonstrate the significantly improved accuracy of the proposed forecast approach.