机械与电子2026，Vol.44Issue(8)：103-109,116,8.
基于BKA-BiLSTM-KD模型的超短期光伏发电功率预测
Ultra-short-term Photovoltaic Power Prediction Based on BKA-BiLSTM-KD Model
摘要
Abstract
To improve the accuracy and shorten the time consumption of ultra-short-term photovol-taic(PV)power forecasting,a method based on BKA-BiLSTM-KD is proposed.First,the K-Nearest Neighbors(KNN)algorithm is employed to clean abnormal data and enhance sample quality,and the ran-dom forest(RF)is utilized for feature selection and combination augmentation.Second,the black-winged kite algorithm(BKA)is used to optimize the prediction model of Bidirectional Long Short-Term Memory(BiLSTM),and the knowledge distillation(KD)is applied to this model to obtain a more computationally efficient student model.Finally,taking the data set from the 17th"The Electrician Mathematical Contest in Modeling"competition and actual power data of a PV station in North China as case samples,four methods with five models,namely BiLSTM,BKA-BiLSTM,WOA-BiLSTM and BKA-BiLSTM-KD,are used for prediction respectively.The prediction results demonstrate that,compared with the BKA-BiLSTM model,the proposed method improves the coefficient of determination(R2)by more than 2.7%and re-duces the prediction time consumption by over 67.8%.It can provide efficient and accurate data support for PV station production planning and power grid dispatching decisions.关键词
功率预测/随机森林/双向长短期记忆网络/黑翅鸢优化算法/知识蒸馏Key words
power forecasting/random forest/bidirectional long short-term memory/black-winged kite algorithm/knowledge distillation分类
信息技术与安全科学引用本文复制引用
侯鹏飞,母宇宸,王月璠,常奇峰,李俏菡..基于BKA-BiLSTM-KD模型的超短期光伏发电功率预测[J].机械与电子,2026,44(8):103-109,116,8.基金项目
河南省自然科学基金青年基金项目(252300420471) （252300420471）
