Ultra-Short-Term Probabilistic Wind Turbine Power Forecast Based on Empirical Dynamic Modeling

This paper proposes a non-parametric probabilistic wind turbine power forecast (WTPF) approach based on the empirical dynamic modeling. According to Takens' theorem, the approach applies historical wind turbine power (WTP) time series to form the reconstructed state space. The moving trajectories of state vectors in the space reflect the evolution laws of WTP, and the divergence of adjacent trajectories indicates the uncertainty existing in the forecast. An enhanced simplex projection algorithm is developed to forecast the probability distribution as well as the prediction interval of WTP with respect to the trajectories. Without any assumptions on the distribution types or physical equations, the proposed approach can overcome the drawbacks of distribution type misspecification or physical model incorrectness in the conventional forecast approaches. The real-time updated state vector set in the reconstructed space further merits the reliability and flexibility of the proposed approach. Case studies employ the proposed approach as well as several benchmarks to achieve 5 min, 15 min, and 30 min ahead probabilistic WTPFs of Penglai wind farm located in Eastern China, and the superior performance demonstrates the effectiveness of the proposed approach.