A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

As a clean energy source, the role of wind power in mix is becoming increasingly important. Reliable and high-quality speed prediction results are key to utilization. The traditional point method cannot effectively analyze uncertainty speed, interval model can provide possible variation range under certain confidence probability supply more uncertain information decision makers. However, previous models generally ignore random characteristics capturing importance objective selection submodels, leading poor results. To address these problems, combined based on data preprocessing, multi-neural network models, multi-objective optimization, an improved proposed. applied five forecasting examples Dalian test accuracy, multi-step ability, universality generalization ability model. experimental show that proposed this study satisfactory for various performance evaluation indexes, has high stability all solutions obtained by Pareto optimal solutions. Thus, it provides reliable reference effective utilization energy.