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This study develops new adaptive learning methods for a dynamic system where the dependency among variables changes over time. In general, many statistical methods focus on characterizing a system or process with historical data and predicting future observations based on a developed time-invariant model. However, for a nonstationary process with time-varying input-to-output relationship, a single baseline curve may not accurately characterize the system's dynamic behavior. This study develops kernel-based nonparametric regression models that allow the baseline curve to evolve over time. Applying the proposed approach to a real wind power system, we investigate the nonstationary nature of wind effect on the turbine response. The results show that the proposed methods can dynamically update the time-varying dependency pattern and can track changes in the operational wind power system.