Short-term load trend prediction of fuel oil based on ARMI-LSTM model

Haobo Liu

doi:10.1117/12.3011495

7 December 2023 Short-term load trend prediction of fuel oil based on ARMI-LSTM model

Haobo Liu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129415B (2023) https://doi.org/10.1117/12.3011495
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In order to improve the forecasting accuracy of fuel oil consumption, taking the short-term load of low sulfur fuel oil in a certain region as an example, the series is divided into linear part and nonlinear part, and the dynamic forecast is carried out by using time series. Through the steps of stationarity test, pattern recognition, parameter estimation and model test of the original sequence, the optimal differential Autoregressive Moving Average (ARIMA) model is obtained. The residual difference between the predicted value and the actual value of the ARIMA model is taken as the input variable of the Long Short Term Memory network (LSTM) model. The predicted value is modified by the output residual of the LSTM model, and the combined model predicted value of the short-term load of fuel oil is formed, and the result is compared with that of the single model. The results show that when p=3 and q=2, the values of AIC and BIC are the smallest, which are 152.61 and 178.54 respectively. The final structure of ARIMA model is (2,1,3). The average relative error and maximum relative error of ARIMA-LSTM combined model were 2.52% and 4.06% respectively, which showed the best prediction effect. The research results can provide practical reference for the fuel oil supply of domestic oil and gas fields and refined gas liquids.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haobo Liu "Short-term load trend prediction of fuel oil based on ARMI-LSTM model", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129415B (7 December 2023); https://doi.org/10.1117/12.3011495

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KEYWORDS

Data modeling

Autoregressive models

Autocorrelation

Sulfur

Pattern recognition

Reflection

Correlation coefficients

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