Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

Abstract

With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.

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“…However, XGBoost and other machine learning models all have hyperparameter optimization problems, that is, the generalization ability of the same input data built by using different hyperparameters is different 29 , 30 . Grid search 31 – 33 , Bayesian optimization 34 , 35 and random search 36 , 37 are all feasible methods to find the optimal hyperparameter. However, when the hyperparameter dimension is too high, the calculation cost of grid search is high, and it is difficult to guarantee to find the optimal hyperparameter combination.…”