Application of WOA-Based LSSVM Model for Wind Speed Prediction in Mianyang, China

In recent years, countries have vigorously developed renewable energy resources to alleviate shortages and improve the environment. Wind energy, as a clean new source, has been increasing its power generation capacity, but wind itself characteristics of volatility instability which makes more difficult. Therefore, novel prediction model based on least squares support vector machine (LSSVM) with whale optimization algorithm (WOA) is proposed in paper accuracy applied speed Mianyang. The implemented python language, then results are evaluated quantitatively using mean absolute percentage error (MAPE), compared models such eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) Random Forest (RF). Further, MAPE about 4.7%-5.5%, can be 6.3% higher than other at best. show that good generalization performance fields future.