基于随机森林回归的船舶特涂维修的日能耗预测

doi:10.11959/j.issn.2096-0271.2024018

Abstract

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).

Key words: energy consumption prediction, random forest regression, LOF algorithm, ship special coating

CLC Number:

TP31

Ruiping GAN, Xinmin REN, Jun JIANG, Peng LI, Xiaobing ZHOU. Prediction of daily energy consumption for ship special coating maintenance based on stochastic forest regression[J]. Big Data Research, 2024, 10(1): 170-184.

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References 20

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影响因素	舱室数量/个	施工面积/m²	工序/道	设备总数/台	总能耗/（kW·h）
萨法	22	27 600	10	40	1 200 162.8
托玛琳	18	24 801	10	52	753 397.85
坦桑石	8	11 091	10	25	255 251.77
丹娜	22	27 454	10	53	828 235.33
古姆达	15	10 753	10	37	492 620.4
西姆斯	22	27 454	10	54	1 081 057.03
黎明之光	14	10 753	10	43	648 553.79
海洋石油116	16	177 573	8	96	1 861 483.63
新道恩	14	8 851	10	30	265 683.9406
雷姆	22	27 454	10	39	908 837.6865

RFR		XGBR		SVR
参数名	参数值	参数名	参数值	参数名	参数值
n_estimators	200	n_estimators	191	C	500
max_depth	14	max_depth	9	kernel	linear
max_features	5	max_features	5	cache_size	500
random_state	50	random_state	100
		eta	0.3

模型	MSE	R²	EV	MAE
LR	0.137	85.95%	86.05%	0.283
PR	0.136	86.45%	86.98%	0.238
RFR	0.067	93.25%	93.29%	0.181
SVR	0.151	84.62%	84.71%	0.267
XGBR	0.080	91.94%	92.11%	0.192

Prediction of daily energy consumption for ship special coating maintenance based on stochastic forest regression

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