摘要
提出了一种基于机器学习中Stacking模型的电池充电能量预测方法,该方法通过对充电数据进行数据探索、特征工程和模型筛选,选取RMSE作为预测结果的评价指标,最后采用Stacking模型对充电能量作出预测。为了验证Stacking模型的预测结果,将Stacking模型与采用单个算法模型的预测结果进行对比,以确保方案的可行性。其结果表明,采用该模型进行预测时,其预测结果的RMSE分值为0.104 1,实现了比单个算法模型更好的预测效果。
In the past,the impact of battery performance evaluation and electric vehicles on the grid has often neglected the impact of battery charging energy,and the charging energy of the battery is also an important indicator in the charging process.To this end,a prediction method of electric charging energy based on the stacking model for machine learning is proposed.The method performs data processing,feature extraction and model screening on the data of charging,and selects the RMSE as the evaluation index of the prediction result.Finally,the stacking model is used to predict the charging energy.In order to verify the prediction results of the Stacking model,the stacking model is compared with the prediction results with a single algorithm model to ensure the feasibility of the scheme.The results show that with the present model for prediction,the RMSE score of the prediction result is 0.1041,which achieves better prediction than that via single algorithm model.
作者
胡杰
蔡世杰
黄腾飞
王成
杜常清
Hu Jie;Cai Shijie;Huang Tengfei;Wang Cheng;Du Changqing(Hubei Key Laboratory of Modem Auto Parts Technology,Wuhan University of Technology,Wuhan 430070,China;Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan University of Technology,Wuhan 430070,China;Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070,China)
出处
《机械科学与技术》
CSCD
北大核心
2020年第6期926-936,共11页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51775393)
柳州市科技计划项目(2018B0301b003)资助。
