Urban energy management system based on intelligent linker

Authors

  • Hongjun Sun Batangas State University,Batangas,Philippines Author
  • Felicito Caluyo Batangas State University,Batangas,Philippines Author
  • Anton Louise De Ocampo Batangas State University,Batangas,Philippines Author
  • Rowell Hernandez Batangas State University,Batangas,Philippines Author
  • Jeffrey Sarmiento Batangas State University,Batangas,Philippines Author

DOI:

https://doi.org/10.56294/saludcyt2024.915

Keywords:

Urban energy management, public facilities, machine learning (ML), multi-source data, bi-fold mechanism-driven convolutional deep network (BMCDN)

Abstract

Introduction: The use of machine learning (ML) approaches to improve energy utilization in smart urban environments has garnered significant attention in recent years.
Objective: This research presents an innovative structure called a bi-fold mechanism-driven convolutional deep network (BMCDN) for estimating the energy performance of urban public facilities in urban energy management systems.
Methods: The suggested method includes two significant phases: (1) feature extraction and fusion, and (2) energy significance estimation. The attention fine-tuned ResNet (N1) processes street-view images to evaluate anticipated market significance levels, while the attention-based Bi-LSTM (N2) integrates cross-domain features using input attention. A decision tree (DT) is used to combine and evaluate the fused information and estimated values, serving as the energy value estimator to determine energy values. Data gathered related to public facilities' energy efficiency from various sources is used to analyze the effectiveness of the suggested framework.  
Results: The research presents an analysis of the performance gains using image-only representations and a proposed approach with morphological traits. The findings demonstrate that incorporating smart urban-related façade images improves the accuracy of the proposed framework and highlights the connection between energy usage and public facilities.
Conclusions: This study shows the potential for significant precision along with rapid inference time in predicting the energy performance of urban public facilities by combining data from numerous sources. 
Keywords: Urban energy management; public facilities; machine learning (ML); multi-source data; bi-fold mechanism-driven convolutional deep network (BMCDN) 

References

Vázquez-Canteli, J.R., Ulyanin, S., Kämpf, J. and Nagy, Z., 2019. Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities. Sustainable cities and society, 45, pp.243-257.https://doi.org/10.1016/j.scs.2018.11.021

Francisco, A., Mohammadi, N. and Taylor, J.E., 2020. Smart city digital twin–enabled energy management: Toward real-time urban building energy benchmarking. Journal of Management in Engineering, 36(2), p.04019045.https://doi.org/10.1061/(ASCE)ME.1943-5479.0000741

Nouriani, A. and Lemke, L., 2022. Vision-based housing price estimation using interior, exterior & satellite images. Intelligent Systems with Applications, 14, p.200081.https://doi.org/10.1016/j.iswa.2022.200081

Law, S., Paige, B. and Russell, C., 2019. Take a look around: using street view and satellite images to estimate house prices. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), pp.1-19.https://doi.org/10.1145/3342240

Egwim, C.N., Alaka, H., Egunjobi, O.O., Gomes, A. and Mporas, I., 2022. Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics. Journal of Engineering, Design and Technology. https://doi.org/10.1108/JEDT-05-2022-0238

Jiang, F., Ma, J., Li, Z. and Ding, Y., 2022. Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model. Energy, 249, p.123631.https://doi.org/10.1016/j.energy.2022.123631

Oraiopoulos, A. and Howard, B., 2022. On the accuracy of urban building energy modelling. Renewable and Sustainable Energy Reviews, 158, p.111976.https://doi.org/10.1016/j.rser.2021.111976

Bhaskar, R.S. and Chakravarthi, V.S., 2021. Predictive Framework for the Urban Environment Monitoring using Artificial Intelligence and Wireless Sensor Network.

Mishra, P. and Singh, G., 2023. Energy Management of Sustainable Smart Cities Using Internet-of-Energy. In Sustainable Smart Cities: Enabling Technologies, Energy Trends and Potential Applications (pp. 143-173). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-031-33354-5_7

Zhang, Y., Teoh, B.K., Wu, M., Chen, J. and Zhang, L., 2023. Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence. Energy, 262, p.125468.https://doi.org/10.1016/j.energy.2022.125468

Fang, X., Gong, G., Li, G., Chun, L., Li, W. and Peng, P., 2021. A hybrid deep transfer learning strategy for short term cross-building energy prediction. Energy, 215, p.119208.https://doi.org/10.1016/j.energy.2020.119208

Pan, F., Lin, G., Yang, Y., Zhang, S., Xiao, J. and Fan, S., 2019. Data-driven demand-side energy management approaches based on the smart energy network. Journal of Algorithms & Computational Technology, 13, p.1748302619891611.http://dx.doi.org/10.1177/1748302619891611

Masood, Z., Ardiansyah and Choi, Y., 2021. Energy-efficient optimal power allocation for swipt based iot-enabled smart meter. Sensors, 21(23), p.7857.https://doi.org/10.3390/s21237857

Fan, C., Sun, Y., Zhao, Y., Song, M. and Wang, J., 2019. Deep learning-based feature engineering methods for improved public facilities energy prediction. Applied energy, 240, pp.35-45.https://doi.org/10.1016/j.apenergy.2019.02.052

Liu, X., Tang, H., Ding, Y. and Yan, D., 2022. Investigating the performance of machine learning models combined with different feature selection methods to estimate the energy consumption of buildings. Energy and Buildings, 273, p.112408.https://doi.org/10.1016/j.enbuild.2022.112408

Pan, Y. and Zhang, L., 2020. Data-driven estimation of building energy consumption with multi-source heterogeneous data. Applied Energy, 268, p.114965.https://doi.org/10.1016/j.apenergy.2020.114965

Ali, U., Bano, S., Shamsi, M.H., Sood, D., Hoare, C., Zuo, W., Hewitt, N. and O'Donnell, J., 2024. Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach. Energy and facilities, 303, p.113768.https://doi.org/10.1016/j.enbuild.2023.113768

Fan, C., Chen, M., Tang, R. and Wang, J., 2022, February. A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. In Building Simulation (Vol. 15, pp. 197-211). Tsinghua University Press. https://doi.org/10.1007/s12273-021-0807-6

Mayer, K., Haas, L., Huang, T., Bernabé-Moreno, J., Rajagopal, R. and Fischer, M., 2023. Estimating urban public facilities energy efficiency from street view imagery, aerial imagery, and land surface temperature data. Applied Energy, 333, p.120542.https://doi.org/10.1016/j.apenergy.2022.120542

Bin, J., Gardiner, B., Li, E. and Liu, Z., 2020. Multi-source urban data fusion for property value assessment: A case study in Philadelphia. Neurocomputing, 404, pp.70-83.https://doi.org/10.1016/j.neucom.2020.05.013

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Published

2024-09-09

How to Cite

1.
Sun H, Caluyo F, De Ocampo AL, Hernandez R, Sarmiento J. Urban energy management system based on intelligent linker. Salud, Ciencia y Tecnología [Internet]. 2024 Sep. 9 [cited 2024 Oct. 14];4:.915. Available from: https://sct.ageditor.ar/index.php/sct/article/view/915