Wind power forecasting can provide effective guidance information for grid connection and optimal scheduling of wind power,and plays an important role in the development and utilization of wind energy.However,accurate wind power forecasting often encounters great challenges due to the inherent intermittency and randomness of wind power.Moreover,the characteristics of wind power data changes over time due to the factors such as seasonality,climate and equipment aging,which causes performance degradation of offline wind power forecasting models.To address these issues,an adaptive wind power forecasting method based on online selective ensemble just-in-time learning(OSEJIT)is proposed.Firstly,we construct a JIT base model library,incorporating similarity and learner perturbation techniques to effectively handle wind power's nonlinearity and time-varying behavior,ensuring reliable forecasting.Secondly,we establish metrics for ensemble effectiveness,utilizing the Friedman test for diversity and prediction accuracy for model selection during online prediction.Subsequently,the final prediction is obtained through adaptive weighted ensemble based on the recent prediction performance of the individual models.To update the base model library while minimizing frequent model reconstruction and resource consumption,a state identification method based on KL divergence is employed.The effectiveness and superiority of the proposed method are validated through a real wind power data set.
关键词
风电功率/预测/自适应算法/过程状态识别/统计假设检验/在线选择性集成/即时学习
Key words
wind power/forecasting/adaptive algorithm/process state identification/statistical hypothesis testing/online selective ensemble/just-in-time learning
维普
万方数据