Processes, Journal Year: 2024, Volume and Issue: 12(8), P. 1749 - 1749

Published: Aug. 20, 2024

Accurate energy consumption prediction is crucial for addressing scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature harnesses the strengths of in handling data. The trained using Adam algorithm parameter optimization facilitated by grid search algorithm. We use latest annual coal, electricity, gasoline Henan Province as application background. model’s performance evaluated against nine fifteen four metrics. Our results show that proposed achieves smallest errors across all metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared other 15 system 9 during testing phase, indicating higher accuracy stronger generalization performance. Additionally, study investigates impact different layers performance, concluding while increasing number initially improves too many lead overfitting.

Language: Английский