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A Short-Term Building Load Prediction Method Based on Modal Decomposition and Deep Learning

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Publicado en:Buildings vol. 15, no. 24 (2025), p. 4455-4486
Autor Principal: Lu Shengze
Outros autores: Yu, Dandan, Ding, Yan, Chen Wanyue, Liang Chuanzhi, Yuan Jihui, Tian Zhe, Lu Yakai
Publicado:
MDPI AG
Materias:
Energy management
Accuracy
Green buildings
Deep learning
Forecasting
Energy efficiency
Signal processing
HVAC equipment
Data processing
Smart buildings
Machine learning
Time series
Cooling loads
Energy consumption
Innovations
Cooling
Predictions
Neural networks
Support vector machines
Decomposition
HVAC
Engineering
Optimization algorithms
Cooling systems
Acceso en liña:Citation/Abstract
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Resumo:Accurate cooling load prediction is essential for energy-efficient HVAC system operation. However, the stochastic and nonlinear nature of load data challenges conventional neural networks, causing prediction delays and errors. To address this, a novel hybrid model is developed. The approach first applies a two-stage decomposition (CEEMDAN with K-means and VMD) to process complex cooling load data. Then, a CNN-BiLSTM network optimized by the Crested Porcupine Optimizer and integrated with an attention mechanism is constructed for prediction. Experimental results demonstrate the model’s high performance, achieving a 96.75% prediction accuracy with a MAPE of 3.25% and an R2 of 0.9929. The proposed model shows strong robustness and generalization, providing a reliable reference for intelligent building energy management.
ISSN:2075-5309
DOI:10.3390/buildings15244455
Fonte:Engineering Database