Prediction Model Integrating Attention Mechanism and BP-LSTM Algorithm for Energy Production
DOI:
https://doi.org/10.13052/dgaej2156-3306.4049Keywords:
Attention mechanism, production prediction, BP, LSTMAbstract
Accurate prediction is crucial for optimizing production plans, improving efficiency, and reducing costs in the energy sector. This study combines a neural network production prediction model with a fusion attention mechanism for energy production. This model uses long short-term memory networks to learn historical production data, integrates back propagation, and introduces attention mechanisms. The results demonstrated that when analyzing different energy sources, the accuracy, the root mean square error, and the prediction time were 0.72, 0.07, and 1.7 seconds, respectively, for a dataset size of 1,000. The proposed model exhibits superior predictive performance across various sources. It provides a more accurate and efficient method for energy production prediction.
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