# Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network
## 1 Introduction
Despite the use of ANN has potential effectiveness on exhaust temperature, a large number of initial parameters are required in this study, and gradient explosion may lead to unsuccessful training, thereby requiring additional time in adjusting the hyperparameters. Moreover, heat load is usually characterized by nonlinear variations; collecting and obtaining these data are necessary under various conditions for analysis and prediction. However, only several influencing factors are analyzed, and the dependencies between the factors are ignored. Considering the shortage of ANN, long short-term memory network (LSTM) model is considered for prediction analysis. LSTM network based on recurrent neural network (RNN) with three additional thresholds is a special form that can solve the problem of gradient explosion and disappearance in training [20]. Continuous development has led to the maturity of this neural network model. However, a large amount of raw data is not effective when processed by LSTM, so it is used together with other methods. The Spearman correlation coefficient method (SR) is utilized in neural networks for feature selection to effectively capture dependencies between variables by analyzing the correlation between two variables and removing redundant information. A LSTM network used to predict passenger flow at stations was proposed by [PERSON] et al. Spearman correlation features were used to select time and space factor data that significantly and effectively affect passenger flow, and the accuracy of the prediction model was improved [21]. Spearman correlation coefficient method was applied by [PERSON] et al. [22] to explore the temporal connection of nonresidential consumers under multiple time series. Spearman's correlation coefficient is a widely used feature selection method. The correlation between multiple information sequences can be effectively analyzed by this method, and the best input of the network model can be provided. However, this purely data-driven method determines dependencies on the basis of only the correlation between feature variables, thereby leading to the exclusion of significant variables. Hence, artificial experience needs to be added when screening features in advance, and significance tests must be performed to ensure that accurate input is provided to the prediction model. As such, a hybrid prediction model incorporating the artificial empirical Spearman correlation coefficient method (AESR) and long short-term memory network (LSTM) is proposed in this study to achieve accurate and stable predictions of exhaust temperature by using the AESR-LSTM model. Redundant information is eliminated through the Spearman correlation coefficient method, and the optimal input is derived by adding artificial empirical supplementary variables while retaining those with high correlation ratings. The hyperparameters are usually selected according to experience and then set in the combination. The combination of cross-validation and grid search methods is used to avoid the blindness of adjusting parameters. The hyperparameters combination of neural network is scientifically optimized and adjusted, and the robustness and accuracy of the prediction model are ensured. After the optimal parameter set is selected by grid search and cross-validation, the model is trained again using the optimal parameters. The trained LSTM model is utilized to predict the exhaust temperature and highlight the advantages of the AESR-LSTM model for data trend prediction compared with other models. The experimental results of the selected prediction model are consistent with the actual values. The prediction result of the model can be sent to the console as a feedback signal, and more convenience and information can be provided to the operator. The predicted results can be used to analyze the combustion conditions in the combustion chamber. Complex models do not need to be used to create analysis, and such signals are difficult to obtain by physical sensors.