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
甘瑞平 1任新民 2姜军 2李鹏 3周小兵1
作者信息
- 1. 云南大学信息学院,云南 昆明 650504
- 2. 友联船厂(蛇口)有限公司,广东 深圳 518067
- 3. 深圳市中科银狐机器人有限公司,广东 深圳 518216
- 折叠
摘要
特殊涂装(简称特涂)维修是修船工作的核心内容,能耗的预测是船舶智能能效优化中的一项重要任务.使用随机森林回归(RFR)模型对船舶特涂维修日能耗进行分析,去除异常值、随机化和标准化数据集,然后使用RFR模型对船舶日能耗历史数据进行训练拟和,利用带交叉验证的网格搜索优化RFR模型,使用优化后的RFR模型对船舶特涂维修日能耗数据进行分析,并与其他模型进行对比实验.结果表明,优化后的RFR模型预测效果优于多种其他模型,R2值达93.25%,均方误差明显更低.
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引用本文复制引用
基金项目
深圳大学稳定保障计划项目(20200829114939001)
深圳信息职业技术学院校级创新科研团队项目(TD2020E001)
珠江三角洲水资源配置工程科研项目(CD88-QT01-2022-0068)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据