Research on predicting heat loads based on extracting temporal and spatial features of multiple buildings using data-driven methods

References

• Ali, A., R. Jayaraman, A. Mayyas, B. Alaifan, and E. Azar. 2023. “Machine Learning as a Surrogate to Building Performance Simulation: Predicting Energy Consumption Under Different Operational Settings.” Energy and Buildings 286: 112940. https://doi.org/10.1016/j.enbuild.2023.112940
   Web of Science ®Google Scholar
• Asadi, S., S. Amiri S, and M. Mottahedi. 2014. “On the Development of Multi-Linear Regression Analysis to Assess Energy Consumption in the Early Stages of Building Design.” Energy and Buildings 85: 246–255. https://doi.org/10.1016/j.enbuild.2014.07.096
   Web of Science ®Google Scholar
• Ayodeji, A., Z. Wang, W. Wang, W. Qin, C. Yang, S. Xu, and X. Liu. 2022. “Causal Augmented ConvNet: A Temporal Memory Dilated Convolution Model for Long-Sequence Time Series Prediction.” ISA Transactions 123: 200–217. https://doi.org/10.1016/j.isatra.2021.05.026
   PubMed Web of Science ®Google Scholar
• Bai, Y., Y. Li, X. Wang, J. Xie, and C. Li. 2016. “Air Pollutants Concentrations Forecasting Using Back Propagation Neural Network Based on Wavelet Decomposition with Meteorological Conditions.” Atmospheric Pollution Research 7: 557–566. https://doi.org/10.1016/j.apr.2016.01.004
   Web of Science ®Google Scholar
• Bujalski, M., P. Madejski, and K. Fuzowski. 2023. “Day-ahead Heat Load Forecasting During the off-Season in the District Heating System Using Generalized Additive Model.” Energy and Buildings 278: 112630. https://doi.org/10.1016/j.enbuild.2022.112630
   Web of Science ®Google Scholar
• Chen, Z., Z. Zhao, Q. Deng, P. Tang, C. Yang, X. Li, and W. Gui. 2023. “A Knowledge Embedded Graph Neural Network-Based Cooling Load Prediction Method Using Dynamic Data Association.” Energy and Buildings 278: 112635. https://doi.org/10.1016/j.enbuild.2022.112635
   Web of Science ®Google Scholar
• Cheng, R., J. Yu, M. Zhang, C. Feng, and W. Zhang. 2022. “Short-term Hybrid Forecasting Model of ice Storage air-Conditioning Based on Improved SVR.” Journal of Building Engineering 50: 104194. https://doi.org/10.1016/j.jobe.2022.104194
   Web of Science ®Google Scholar
• Dahl, M., A. Brun, O. S. Kirsebom, and B. G. Andresen. 2018. “Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data.” Energies 11 (7): 1678. https://doi.org/10.3390/en11071678
   Web of Science ®Google Scholar
• Daubechies, I., J. Lu, and H. T. Wu. 2011. “Synchrosqueezed Wavelet Transforms: An Empirical Mode Decomposition-Like Tool.” Applied and Computational Harmonic Analysis 30 (2): 243–261. https://doi.org/10.1016/j.acha.2010.08.002
   Web of Science ®Google Scholar
• Dong, B., Z. Li, S. M. M. Rahman, and R. Vega. 2016. “A Hybrid Model Approach for Forecasting Future Residential Electricity Consumption.” Energy and Buildings 117: 341–351. https://doi.org/10.1016/j.enbuild.2015.09.033
   Web of Science ®Google Scholar
• Dotzauer, E. 2002. “Simple Model for Prediction of Loads in District-Heating Systems.” Applied Energy 73 (3-4): 277–284. https://doi.org/10.1016/s0306-2619(02)00078-8.
   Web of Science ®Google Scholar
• Fang, L., and B. He. 2023. “A Deep Learning Framework Using Multi-Feature Fusion Recurrent Neural Networks for Energy Consumption Forecasting.” Applied Energy 348: 121563. https://doi.org/10.1016/j.apenergy.2023.121563
   Web of Science ®Google Scholar
• Fang, T., and R. Lahdelma. 2016. “Evaluation of a Multiple Linear Regression Model and SARIMA Model in Forecasting Heat Demand for District Heating System.” Applied Energy 179: 544–522. https://doi.org/10.1016/j.apenergy.2016.06.133.
   Web of Science ®Google Scholar
• Ghayekhloo, M., B. Menhaj M, and M. Ghofrani. 2015. “A Hybrid Short-Term Load Forecasting with a new Data Preprocessing Framework.” Electric Power Systems Research 119: 138–148. https://doi.org/10.1016/j.epsr.2014.09.002
   Web of Science ®Google Scholar
• Gong, M., Y. Zhao, J. Sun, C. Han, G. Sun, and B. Yan. 2022. “Load Forecasting of District Heating System Based on Informer.” Energy 253: 124179. https://doi.org/10.1016/j.energy.2022.124179
   Web of Science ®Google Scholar
• Idowu, S., S. Saguna, C. Ahlund, and O. Schelén. 2016. “Applied Machine Learning: Forecasting Heat Load in District Heating System.” Energy and Buildings 133: 478–488. https://doi.org/10.1016/j.enbuild.2016.09.068
   Web of Science ®Google Scholar
• Jia, Y., J. Wang, R. Hosseini M, W. Shou, P. Wu, and M. Chao. 2023, Sep 9. “Temporal Graph Attention Network for Building Thermal Load Prediction.” Energy and Buildings, 113507. https://doi.org/10.1016/j.enbuild.2023.113507
   Google Scholar
• Kong, F., J. Song, and Z. Yang. 2022. “A Daily Carbon Emission Prediction Model Combining two-Stage Feature Selection and Optimized Extreme Learning Machine.” Environmental Science and Pollution Research 29 (58): 87983–87997. https://doi.org/10.1007/s11356-022-21277-9
   PubMed Web of Science ®Google Scholar
• Liao, L., Z. Hu, Y. Zheng, S. Bi, F. Zou, H. Qiu, and M. Zhang. 2022. “An Improved Dynamic Chebyshev Graph Convolution Network for Traffic Flow Prediction with Spatial-Temporal Attention.” Applied Intelligence 52: 16104–16116. https://doi.org/10.1007/s10489-021-03022-w
   Web of Science ®Google Scholar
• Lin, J., J. Ma, J. Zhu, and Y. Gui. 2022. “Short-term Load Forecasting Based on LSTM Networks Considering Attention Mechanism.” International Journal of Electrical Power & Energy Systems 137: 107818. https://doi.org/10.1016/j.ijepes.2021.107818
   Web of Science ®Google Scholar
• Liu, G., S. Fomel, and X. Chen. 2011. “Time-frequency Analysis of Seismic Data Using Local Attributes.” Geophysics 76 (6): 23–P34. https://doi.org/10.1190/geo2010-0185.1
   Web of Science ®Google Scholar
• Lu, C., S. Li, and Z. Lu. 2022. “Building Energy Prediction Using Artificial Neural Networks: A Literature Survey.” Energy and Buildings 262: 111718. https://doi.org/10.1016/j.enbuild.2021.111718
   Web of Science ®Google Scholar
• Ma, Y., H. Lou, M. Yan, F. Sun, and G. Li. 2024, Apr 1. “Spatio-temporal Fusion Graph Convolutional Network for Traffic Flow Forecasting.” Information Fusion 104: 102196.
   Web of Science ®Google Scholar
• Massidda, L., and M. Marrocu. 2023. “Total and Thermal Load Forecasting in Residential Communities Through Probabilistic Methods and Causal Machine Learning.” Applied Energy 351: 121783. https://doi.org/10.1016/j.apenergy.2023.121783
   Web of Science ®Google Scholar
• Protić, M., S. Shamshirband, H. Anisi M, D. Petković, D. Mitić, M. Raos, and K. Alam. 2015. “Appraisal of Soft Computing Methods for Short Term Consumers’ Heat Load Prediction in District Heating Systems.” Energy 82: 697–704. https://doi.org/10.1016/j.energy.2015.01.079
   Web of Science ®Google Scholar
• Redmon J, Farhadi A. 2017 YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition. 7263–7271.
   Google Scholar
• Shen, L., and Y. Wang. 2022. “TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting.” Neurocomputing 480: 131–145. https://doi.org/10.1016/j.neucom.2022.01.039
   Web of Science ®Google Scholar
• Song, J., L. Zhang, G. Xue, Y. Ma, S. Gao, and Q. Jiang. 2021. “Predicting Hourly Heating Load in a District Heating System Based on a Hybrid CNN-LSTM Model.” Energy and Buildings 243: 110998. https://doi.org/10.1016/j.enbuild.2021.110998
   Web of Science ®Google Scholar
• Sun, Y., F. Haghighat, and B. C. M. Fung. 2020. “A Review of the-State-of-the-art in Data-Driven Approaches for Building Energy Prediction.” Energy and Buildings 221: 110022. https://doi.org/10.1016/j.enbuild.2020.110022
   Web of Science ®Google Scholar
• Sun, C., Y. Liu, S. Cao, X. Gao, G. Xia, C. Qi, and X. Wu. 2022. “Integrated Control Strategy of District Heating System Based on Load Forecasting and Indoor Temperature Measurement.” Energy Reports 8: 8124–8139. https://doi.org/10.1016/j.egyr.2022.06.031
   Web of Science ®Google Scholar
• Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. “Rethinking the Inception Architecture for Computer Vision.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826.
   Google Scholar
• Tan, M., C. Hu, J. Chen, L. Wang, and Z. Li. 2022. “Multi-node Load Forecasting Based on Multi-Task Learning with Modal Feature Extraction.” Engineering Applications of Artificial Intelligence 112: 104856.
   Web of Science ®Google Scholar
• Torres J., F., D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso. 2021. “Deep Learning for Time Series Forecasting: A Survey.” Big Data 9 (1): 3–21. https://doi.org/10.1089/big.2020.0159
   PubMed Web of Science ®Google Scholar
• Veličković, P., G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2017. “Graph Attention Networks.” arXiv preprint arXiv:1710.10903.
   Google Scholar
• Verhelst, J., G. Van Ham, D. Saelens, and L. Helsen. 2017. “Model Selection for Continuous Commissioning of HVAC-Systems in Office Buildings: A Review.” Renewable and Sustainable Energy Reviews 76: 673–686. https://doi.org/10.1016/j.rser.2017.01.119
   Web of Science ®Google Scholar
• Wan, A., Q. Chang, L. B. Khalil A, and J. He. 2023. “Short-term Power Load Forecasting for Combined Heat and Power Using CNN-LSTM Enhanced by Attention Mechanism.” Energy 282: 128274. https://doi.org/10.1016/j.energy.2023.128274
   Web of Science ®Google Scholar
• Wang, Z., X. Liu, Y. Huang, P. Zhang, and Y. Fu. 2023. “A Multivariate Time Series Graph Neural Network for District Heat Load Forecasting.” Energy 278: 127911. https://doi.org/10.1016/j.energy.2023.127911
   Web of Science ®Google Scholar
• Wang, R., J. Wang, and Y. Xu. 2019. “A Novel Combined Model Based on Hybrid Optimization Algorithm for Electrical Load Forecasting.” Applied Soft Computing 82: 105548. https://doi.org/10.1016/j.asoc.2019.105548
   Web of Science ®Google Scholar
• Wu, H.-T., Y.-H. Chan, Y.-T. Lin, and Y.-H. Yeh. 2014. “Using Synchrosqueezing Transform to Discover Breathing Dynamics from ECG Signals.” Applied and Computational Harmonic Analysis 36 (2): 354–359. https://doi.org/10.1016/j.acha.2013.07.003
   Web of Science ®Google Scholar
• Wu, Z., F. Pan, D. Li, H. He, T. Zhang, and S. Yang. 2022. “Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network.” Sustainability 14 (20): 13022. https://doi.org/10.3390/su142013022
   Web of Science ®Google Scholar
• Xu, D., Z. Lin, L. Zhou, H. Li, and B. Niu. 2022. “A GATs-GAN Framework for Road Traffic States Forecasting.” Transportmetrica B: Transport Dynamics 10 (1): 718–730. https://doi.org/10.1080/21680566.2022.2030825
   Web of Science ®Google Scholar
• Xue, P., Y. Jiang, Z. Zhou, X. Chen, X. Fang, and J. Liu. 2019. “Multi-step Ahead Forecasting of Heat Load in District Heating Systems Using Machine Learning Algorithms.” Energy 188 (116085).
   Google Scholar
• Yu, D., T. Liu, K. Wang, K. Li, M. Mercangöz, J. Zhao, and R. Zhao. 2024. “Transformer Based Day-Ahead Cooling Load Forecasting of Hub Airport Air-Conditioning Systems with Thermal Energy Storage.” Energy and Buildings 308: 114008. https://doi.org/10.1016/j.enbuild.2024.114008
   Web of Science ®Google Scholar
• Yue, W., Q. Liu, Y. Ruan, F. Qian, and H. Meng. 2022. “A Prediction Approach with Mode Decomposition-Recombination Technique for Short-Term Load Forecasting.” Sustainable Cities and Society 85: 104034.
   Web of Science ®Google Scholar
• Zhang, Y., J. Xia, H. Fang, Y. Jiang, and Z. Liang. 2020. “Field Tests on the Operational Energy Consumption of Chinese District Heating Systems and Evaluation of Typical Associated Problems.” Energy and Buildings 224: 110269.
   Web of Science ®Google Scholar
• Zhao, A., L. Mi, X. Xue, J. Xi, and Y. Jiao. 2022. “Heating Load Prediction of Residential District Using Hybrid Model Based on CNN.” Energy and Buildings 266: 112122. https://doi.org/10.1016/j.enbuild.2022.112122
   Web of Science ®Google Scholar
• Zhou, C., Z. Fang, X. Xu, X. Zhang, Y. Ding, and X. Jiang. 2020. “Using Long Short-Term Memory Networks to Predict Energy Consumption of Air-conditioning Systems.” Sustainable Cities and Society 55: 102000. https://doi.org/10.1016/j.scs.2019.102000
   Web of Science ®Google Scholar
• Zhu, J., H. Dong, W. Zheng, S. Li, Y. Huang, and L. Xi. 2022. “Review and Prospect of Data-Driven Techniques for Load Forecasting in Integrated Energy Systems.” Applied Energy 321: 119269. https://doi.org/10.1016/j.apenergy.2022.119269
   Web of Science ®Google Scholar