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The Kernel ridge regression (<inline-formula><math display="inline"> <semantics> <mrow> <mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>) model aims to find the hidden nonlinear structure in raw data. It makes an assumption that the noise in data satisfies the Gaussian model. However, it was pointed out that the noise in wind speed/power forecasting obeys the Beta distribution. The classic regression techniques are not applicable to this case. Hence, we derive the empirical risk loss about the Beta distribution and propose a technique of the kernel ridge regression model based on the Beta-noise (<inline-formula><math display="inline"> <semantics> <mrow> <mi>B</mi> <mi>N</mi></mrow></semantics></math></inline-formula>-<inline-formula><math display="inline"><semantics><mrow><mi>K</mi> <mi>R</mi> <mi>R</mi></mrow></semantics></math></inline-formula>). The numerical experiments are carried out on real-world data. The results indicate that the proposed technique obtains good performance on short-term wind speed forecasting.