Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution

Abstract

At present, environmental pollution, climate warming and other problems are becoming more and more serious. And wind energy is pollution-free and never be exhausted, so it can make a major contribution to the global energy transformation. However, its random fluctuations and uncertainties bring adverse effects to the power system and endanger the safety of the power grid. Therefore, this paper combines artificial intelligence methods with statistical knowledge, and proposes a new interval prediction model based on the Fast Correlation Based Filter (FCBF) algorithm, the optimized Radial Basis Function (RBF) model and Fourier distribution for wind speed. Firstly considering environmental factors, this paper studies multi-factor wind speed prediction and applies the FCBF algorithm to filter the factors that affect the wind change. After that, this paper applies the idea of the Extremal Optimization (EO) to improve the Particle Swarm Optimization (PSO) and constructs a new EPSO optimization model for optimizing the RBF model. Next, using the Fourier function to fit the error probability distribution, and the wind speed interval is estimated based on point prediction results. Finally, the actual data of Changma Wind Farm is used for experiments to verify the feasibility and effectiveness of the proposed model. And through experimental results and comparison, it can be concluded: (1) Using the FCBF algorithm to select input variables can reduce redundant variables and lay a good foundation for subsequent prediction; (2) Applying the constructed EPSO-RBF model to predict wind speed, and the maximum and average value of the prediction error are only 0.8430 m/s, 0.1749 m/s, which is significantly better than several other traditional neural network models; (3) Introducing the Fourier function into the wind speed interval prediction, even at the 80% confidence level, the average width of the interval prediction is less than 3 m/s, and the coverage rate is higher than 90%.