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

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I whakaputaina i:	Buildings vol. 15, no. 24 (2025), p. 4455-4486
Kaituhi matua:	Lu Shengze
Ētahi atu kaituhi:	Yu, Dandan, Ding, Yan, Chen Wanyue, Liang Chuanzhi, Yuan Jihui, Tian Zhe, Lu Yakai
I whakaputaina:	MDPI AG
Ngā marau:	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
Urunga tuihono:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Whakaahuatanga
Whakarāpopotonga:	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
Puna:	Engineering Database