A Short-Term Building Load Prediction Method Based on Modal Decomposition and Deep Learning
Guardado en:
| 發表在: | Buildings vol. 15, no. 24 (2025), p. 4455-4486 |
|---|---|
| 主要作者: | |
| 其他作者: | , , , , , , |
| 出版: |
MDPI AG
|
| 主題: | |
| 在線閱讀: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| 標簽: |
沒有標簽, 成為第一個標記此記錄!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3286268202 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-5309 | ||
| 024 | 7 | |a 10.3390/buildings15244455 |2 doi | |
| 035 | |a 3286268202 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Lu Shengze |u School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China | |
| 245 | 1 | |a A Short-Term Building Load Prediction Method Based on Modal Decomposition and Deep Learning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Energy management | ||
| 653 | |a Accuracy | ||
| 653 | |a Green buildings | ||
| 653 | |a Deep learning | ||
| 653 | |a Forecasting | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Signal processing | ||
| 653 | |a HVAC equipment | ||
| 653 | |a Data processing | ||
| 653 | |a Smart buildings | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Cooling loads | ||
| 653 | |a Energy consumption | ||
| 653 | |a Innovations | ||
| 653 | |a Cooling | ||
| 653 | |a Predictions | ||
| 653 | |a Neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Decomposition | ||
| 653 | |a HVAC | ||
| 653 | |a Engineering | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Cooling systems | ||
| 700 | 1 | |a Yu, Dandan |u School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China | |
| 700 | 1 | |a Ding, Yan |u School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China | |
| 700 | 1 | |a Chen Wanyue |u School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China | |
| 700 | 1 | |a Liang Chuanzhi |u Center of Science and Technology & Industrialization Development, Ministry of Housing and Urban-Rural Development, Beijing 100835, China | |
| 700 | 1 | |a Yuan Jihui |u Department of Living Environment Design, Graduate School of Human Life and Ecology, Osaka Metropolitan University, Osaka 558-8585, Japan | |
| 700 | 1 | |a Tian Zhe |u School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China | |
| 700 | 1 | |a Lu Yakai |u School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China | |
| 773 | 0 | |t Buildings |g vol. 15, no. 24 (2025), p. 4455-4486 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286268202/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286268202/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286268202/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |