大数据2024，Vol.10Issue(1)：170-184,15.DOI:10.11959/j.issn.2096-0271.2024018
基于随机森林回归的船舶特涂维修的日能耗预测
Prediction of daily energy consumption for ship special coating maintenance based on stochastic forest regression
摘要
Abstract
Predicting energy consumption is an important task in the intelligent energy efficiency optimization of ship maintenance,with special coating(spec coat)being the core aspect.In this experiment,the random forest regression(RFR)model was employed to analyze the daily energy consumption of ship maintenance for special coating.The dataset was preprocessed by removing outliers,randomizing and standardizing the data.Subsequently,the RFR model was trained and fitted using historical data of daily energy consumption in ship maintenance.The RFR model was optimized using grid search with cross-validation,and analysis of daily energy consumption data for ship special coating maintenance using optimized RFR model.Comparative experiments were conducted with other models.The results revealed that the optimized RFR model outperformed several other models,achieving an R-squared value of 93.25%and significantly lower mean squared error(MSE).关键词
能耗预测/随机森林回归/LOF算法/船舶特涂Key words
energy consumption prediction/random forest regression/LOF algorithm/ship special coating分类
信息技术与安全科学引用本文复制引用
甘瑞平,任新民,姜军,李鹏,周小兵..基于随机森林回归的船舶特涂维修的日能耗预测[J].大数据,2024,10(1):170-184,15.基金项目
深圳大学稳定保障计划项目(No.20200829114939001) （No.20200829114939001）
深圳信息职业技术学院校级创新科研团队项目(No.TD2020E001) （No.TD2020E001）
珠江三角洲水资源配置工程科研项目(No.CD88-QT01-2022-0068) Shenzhen University Stability Support Plan(No.20200829114939001),Project of Shenzhen Institute of Information Technology School-level Innovative,Scientific Research Team(No.TD2020E001),The Pearl River Delta Water Resources Allocation Engineering Scientific Research Project(No.CD88-QT01-2022-0068) （No.CD88-QT01-2022-0068）
