# Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network
## 4 Conclusions  
According to the data set collected in the marine cabin system, an AESR-LSTM data trend prediction model with artificial experience is constructed in this study. The model can be used for heat load prediction, fault detection, and diagnosis of marine diesel engines. Spearman correlation coefficient method is used to collect relevant raw data for feature selection, and the optimal input is selected by artificial empirical and significance check. The cross-validation and grid search methods are combined, and the hyperparameters are adjusted scientifically to avoid the randomness of the validation set. After the optimal parameter set is selected by grid search and cross-validation, the model is trained again with the optimal parameters, and the test set data is input into the training model to obtain the prediction results. The findings are subsequently compared and analyzed with those of other prediction models. (1) The Spearman correlation coefficient method incorporating artificial experience was proposed to select features on the basis of operational monitoring data collected from the sensors. The correlation, redundancy, and significance of variable sets are analyzed separately, and the nine monitoring characteristic parameters with the maximum influence on the exhaust temperature are selected. Data-driven analysis and human experience are combined to provide optimal input features for the predictive models. (2) The LSTM prediction model is trained with parameter tuning in combination with cross-validation grid search to obtain the prediction and evaluation metrics. The results and indicators of several models were compared. The results show that predicted value of AESR-LSTM are closest to the true value, and its evaluation indicators MAPE, MAE and RMSE are the best, which are 0.089, 10.5403, and 27.5408, respectively. (3) The shortcomings of only using a single method can be overcome by the fusion of multiple methods, and the data can be scientifically and effectively screened to improve the effectiveness of the model in data prediction and fault diagnosis of marine diesel engines. Thus, the hybrid algorithm model is stable, and the error tolerance of the prediction results is reduced. Figure 12: Comparative results histogram of model evaluation metrics: (**a**) MAPE value histogram of different models, (**b**) MAE value histogram of different models, and (**c**) RMSE value histogram of different models. (4) The proposed method is based on the mechanism and data of the ship. All factors that may cause thermal load failure of the diesel engine are taken into account and can be used to analyze and refer to the working performance of the marine diesel engine. The prediction data can achieve effective fault detection and maintenance of ships for the implementation of preemptive corrective measures before ship failure, prevent ship downtime due to damaged components caused by excessive heat load, improve fuel economy and equipment reliability of ship diesel engines, and reduce economic losses. A novel method combining artificial experience and data-driven is proposed. The selected optimal feature set is input into the model for prediction, and the better prediction results are obtained. As such, a feasible extended method of machine learning in marine diesel engine thermal load prediction and fault diagnosis is provided. Future research can focus on the optimization of methods, better operation parameter combination will be obtained through data mining techniques, and independent fault detection system will be developed to provide more convenience and information for ship operators. Conceptualization, R.Z. ; methodology, R.Z. ; software, R.Z. ; validation, R.Z. ; formal analysis, R.Z. ; investigation, R.Z. ; resources, R.Z. ; data curation, R.Z. and J.C.; writing--original draft preparation, R.Z. ; writing--review and editing, R.Z. and J.C.; visualization, R.Z. and X.W. ; supervision, J.C. and G.Z. ; project administration, J.C. and X.Y.