Wind power forecasting is a crucial part for the safe and stable operation of wind power integration, which is under the influence of different factors such as wind speed, wind direction, atmospheric pressure. These factors bring randomness and volatility to wind power which makes it less predictable. While, there are very limited studies on describing the uncertainty of wind power. Therefore, to providing additional information on the uncertainty and volatility, a kernel-based on Gaussian Process Regression (GPR) incorporating the hyper-parame...
