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- Title
基于 BO-LSTM 的天然气处理厂负荷率预测模型.
- Authors
刘行; 王秋晨; 文韵豪; 王艺; 巴玺立
- Abstract
To optimize the production schedule of natural gas treatment plants and fill in a gap in the load rate prediction model, this study introduces a natural gas treatment plant load rate prediction model based on Bayesian optimization and long short-term memory (BO-LSTM). The LSTM model captures temporal patterns arising from maintenance schedules and daily processing volumes. Concurrently, the Bayesian optimization refines the LSTM network's structure, hidden layers, neuron counts, initial learning rate, and regularization coefficient, mitigating prediction fluctuations due to parameter variations. The load rates of treatment plants are classified into fluctuating type and stable type. The results indicate that the LSTM model outperformed other traditional forecasting models regarding prediction accuracy. Furthermore, the BO-LSTM model stands out, boasting the lowest mean absolute error (MAE) and root mean squared error (RMSE) values, which translates to superior prediction accuracy and robust versatility. Impressively, the BO-LSTM model achieves a 57.8% improvement in MAE and a 30. 1% boost in RMSE values when compared with the conventional LSTM model. This advanced load rate prediction model offers data support for the operational and decision-making processes of the treatment plant, ensuring consistent prediction accuracy and adaptability.
- Publication
Natural Gas & Oil, 2023, Vol 41, Issue 5, p122
- ISSN
1006-5539
- Publication type
Article
- DOI
10.3969/j.issn.1006-5539.2023.05.018