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

Abstract

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

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

“…6 represents a comparison study of the IWSP-CSODL model with other models under scenario 1. The experimental values ensured the effectual predictive outcomes of the IWSP-CSODL model with minimal values of MSE, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) [43][44][45][46]. Concerning MSE, the IWSP-CSODL model has offered a lower MSE of 0.5318.…”