Content area
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.
Details
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
1 School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
2 School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China, Tianjin Key Laboratory of Built Environment and Energy Application, Tianjin University, Tianjin 300350, China
3 School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
4 Center of Science and Technology & Industrialization Development, Ministry of Housing and Urban-Rural Development, Beijing 100835, China
5 Department of Living Environment Design, Graduate School of Human Life and Ecology, Osaka Metropolitan University, Osaka 558-8585, Japan
6 School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China