Journal of Applied Science and Engineering

Published by Tamkang University Press

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Xiaohui ZhangThis email address is being protected from spambots. You need JavaScript enabled to view it. and Lili Pei

College of Information Engineering, Tangshan Polytechnic College, Tangshan, 063299, China


 

 

Received: December 11, 2023
Accepted: April 1, 2024
Publication Date: May 23, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202503_28(3).0016  


The imperative of accurately assessing the Cooling Load, which denotes the requisite energy to regulate temperatures within a given space, underscores its fundamental role in energy conservation, proficient management, and strategic planning. Precise prognostications of energy consumption are pivotal for optimizing resource allocation and fostering sustainability. The continuous refinement of predictive models is indispensable for bolstering the efficacy of energy systems in tandem with technological advancements. This research presents hybrid machine learning models integrated with advanced optimization techniques tailored for accurately predicting Cooling Load in buildings. By synergizing machine learning and optimization, it strives to pioneer predictive and managerial methods for cooling energy requirements, thus enhancing overall sustainability in the built environment. To attain this objective, the research employs the Gaussian Process Regression model in conjunction with both the Zebra Optimization Algorithm and the Gold Rush Optimizer. A comprehensive comparative analysis was conducted to meticulously scrutinize the predictive capabilities of the proposed models. As evidenced by the results, the GPZO (GPR+ZOA) model emerged as the frontrunner, attaining an outstanding R2 value of 99.6 percent. Furthermore, it showcased the lowest RMSE value, an impressive 0.596. These compelling findings unequivocally highlight the superior predictive accuracy and optimization proficiency of the GPZO model in accurately forecasting cooling load.


Keywords: Heating Energy Consumption; Cooling Load; Gaussian Process Regression; African Vultures Optimization Algorithm; Sand Cat Swarm Optimization.


  1. [1] J. Leitao, P. Gil, B. Ribeiro, and A. Cardoso, (2020) “A survey on home energy management" IEEE Access 8: 5699–5722.
  2. [2] H. Gong, V. Rallabandi, M. L. McIntyre, E. Hossain, and D. M. Ionel, (2021) “Peak reduction and long term load forecasting for large residential communities including smart homes with energy storage" IEEE Access 9: 19345–19355. DOI: 10.1109/ACCESS.2021.3052994.
  3. [3] M. A. Hannan, M. Faisal, P. J. Ker, L. H. Mun, K. Parvin, T. M. I. Mahlia, and F. Blaabjerg, (2018) “A review of internet of energy based building energy management systems: Issues and recommendations" Ieee Access 6: 38997–39014. DOI: 10.1109/ACCESS.2018.2852811.
  4. [4] O. Sadeghian, A. Moradzadeh, B. MohammadiIvatloo, M. Abapour, A. Anvari-Moghaddam, J. S. Lim, and F. P. G. Marquez, (2021) “A comprehensive review on energy saving options and saving potential in low voltage electricity distribution networks: Building and public lighting" Sustainable Cities and Society 72: 103064. DOI: 10.1016/j.scs.2021.103064.
  5. [5] O. Sadeghian, A. Moradzadeh, B. MohammadiIvatloo, M. Abapour, and F. P. G. Marquez, (2020) “Generation units maintenance in combined heat and power integrated systems using the mixed integer quadratic programming approach" Energies 13: 2840. DOI: 10.3390/en13112840.
  6. [6] H. Nami, A. Anvari-Moghaddam, and A. Arabkoohsar, (2020) “Application of CCHPs in a centralized domestic heating, cooling and power network—Thermodynamic and economic implications" Sustainable Cities and Society 60: 102151. DOI: 10.1016/j.scs.2020.102151.
  7. [7] Q. Chen, M. Xia, T. Lu, X. Jiang, W. Liu, and Q. Sun, (2019) “Short-term load forecasting based on deep learning for end-user transformer subject to volatile electric heating loads" IEEE Access 7: 162697–162707. DOI: 10.1109/ACCESS.2019.2949726.
  8. [8] T. Hong, C. Koo, J. Kim, M. Lee, and K. Jeong, (2015) “A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building’s dynamic energy performance: Focused on the operation and maintenance phase" Applied Energy 155: 671–707. DOI: 10.1016/j.apenergy.2015.06.043.
  9. [9] J.-S. Chou and D.-K. Bui, (2014) “Modeling heating and cooling loads by artificial intelligence for energy-efficient building design" Energy and Buildings 82: 437–446. DOI: 10.1016/j.enbuild.2014.07.036.
  10. [10] A. Moradzadeh, H. Moayyed, S. Zakeri, B. Mohammadi-Ivatloo, and A. P. Aguiar, (2021) “Deep learning-assisted short-term load forecasting for sustainable management of energy in microgrid" Inventions 6: 15.
  11. [11] S. Kumar, S. K. Pal, and R. P. Singh, (2018) “A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes" Energy and Buildings 176: 275–286. DOI: 10.1016/j.enbuild.2018.06.056.
  12. [12] S. Chen, X. Zhang, S. Wei, T. Yang, J. Guan, W. Yang, L. Qu, and Y. Xu, (2019) “An energy planning oriented method for analyzing spatial-temporal characteristics of electric loads for heating/cooling in district buildings with a case study of one university campus" Sustainable Cities and Society 51: 101629. DOI: 10.1016/j.scs.2019.101629.
  13. [13] S. S. Roy, P. Samui, I. Nagtode, H. Jain, V. Shivaramakrishnan, and B. Mohammadi-Ivatloo, (2020) “Forecasting heating and cooling loads of buildings: A comparative performance analysis" Journal of Ambient Intelligence and Humanized Computing 11: 1253–1264. DOI: 10.1007/s12652-019-01317-y.
  14. [14] R. Yao, B. Li, and K. Steemers, (2005) “Energy policy and standard for built environment in China" Renewable Energy 30: 1973–1988.
  15. [15] O. Hammoody, J. A. Al-Somaydaii, F. M. S. AlZwainy, and G. Hayder, (2022) “Forecasting And Determining of Cost Performance Index of Tunnels Projects Using Artificial Neural Networks" International Journal for Computational Civil and Structural Engineering: DOI: 10.22337/2587-9618-2022-18-1-51-60.
  16. [16] S. K. Zamim, N. S. Faraj, I. A. Aidan, F. M. S. AlZwainy, M. A. AbdulQader, and I. A. Mohammed, (2019) “Prediction of dust storms in construction projects using intelligent artificial neural network technology" Periodicals of Engineering and Natural Sciences 7: 1659–1666.
  17. [17] F. M. S. Al-Zwainy, F. M. Al-Zwainy, H. Ala, and H. F. Ibraheem, (2020) “Predicting productivity in construction industry utilizing multiple linear regression technique and artificial neural network technique: A review for research and applications" International Journal of Research in Advanced Engineering and Technology 7:
  18. [18] A. A. Hameed and F. M. S. Al-Zwainy, (2022) “Development of duration estimation model for irrigation and drainage projects using neural Network" NeuroQuantology 20: 3041.
  19. [19] I. A. Aidan, D. Al-Jeznawi, and F. M. S. Al-Zwainy, (2020) “Predicting earned value indexes in residential complexes’ construction projects using artificial neural network model" International Journal of Intelligent Engineering and Systems 13: 248–259.
  20. [20] F. K. Jaber, F. M. S. Al-Zwainy, and S. W. Hachem, (2019) “Optimizing of predictive performance for construction projects utilizing support vector machine technique" Cogent Engineering 6: 1685860. DOI: 10.1080/23311916.2019.1685860.
  21. [21] Q. Khan, G. Hayder, and F. M. S. Al-Zwainy, (2023) “River Water Suspended Sediment Predictive Analytics Using Artificial Neural Network and Convolutional Neural Network Approach: A Review" Sustainability Challenges and Delivering Practical Engineering Solutions: Resources, Materials, Energy, and Buildings: 51–56. DOI: 10.1007/978-3-031-26580-8_10.
  22. [22] F. K. Jaber, N. A. Jasim, and F. M. S. Al-Zwainy, (2020) “Forecasting techniques in construction industry: earned value indicators and performance models" Przegl ˛ad Naukowy. Inzynieria i Kształtowanie ˙ Srodowiska ´ 29:
  23. [23] F. Al-Zwainy, M. Shaban, and A. Hassan, (2023) “Development intelligent prediction models for earned value indicators in wastewater treatment plants projects" Journal of Al-Azhar University Engineering Sector 18: 1009–1029.
  24. [24] F. M. S. Al-Zwainy and I. A.-A. Aidan, (2017) “Forecasting the cost of structure of infrastructure projects utilizing artificial neural network model (highway projects as case study)" Indian Journal of Science and Technology:
  25. [25] F. M. Al-Zwainy, (2014) “Development of the mathematical model for predicating the construction productivity in iraq using the artificial neural perceptron network" Journal of Engineering and Sustainable Development 18: Ar–1.
  26. [26] F. M. S. Al-Zwainy, R. I. K. Zaki, A. M. Al-saadi, and H. F. Ibraheem, (2018) “Validity of artificial neural modeling to estimate time-dependent deflection of reinforced concrete beams" Cogent Engineering 5: 1477485.
  27. [27] N. A. Jasim, S. M. Maruf, H. S. M. Aljumaily, and F. Al-Zwainy, (2020) “Predicting index to complete schedule performance indicator in highway projects using artificial neural network model" Archives of Civil Engineering 66:
  28. [28] A. Al-Rweis, L. Al-Omari, and K. Abdul-Aziz, (2024) “Impact of smoking on Galectin-3 and GDF-15 among pregnant women" Tamjeed Journal of Healthcare Engineering and Science Technology 2: 1–12.
  29. [29] M. L. Thivagar, A. S. Al-Obeidi, B. Tamilarasan, and A. A. Hamad. “Dynamic analysis and projective synchronization of a new 4D system”. In: Springer, 2022, 323–332.
  30. [30] L. Wang, (2023) “Application of time series models in forecasting the efficiency of regional digital economy development" Journal of Applied Science and Engineering 27: 2971–2979. DOI: 10.6180/jase.202408_27(8).0005.
  31. [31] N. T. H. Thu, P. Q. Bao, and P. N. Van, (2023) “A hybrid model of decomposition, extended Kalman filter and autoregressive-long short-term memory network for hourly day ahead wind speed forecasting" J. Appl. Sci. Eng. 27: 3063–3071.
  32. [32] A. Moradzadeh, O. Sadeghian, K. Pourhossein, B. Mohammadi-Ivatloo, and A. Anvari-Moghaddam, (2020) “Improving residential load disaggregation for sustainable development of energy via principal component analysis" Sustainability 12: 3158.
  33. [33] J. Zhao and X. Liu, (2018) “A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis" Energy and Buildings 174: 293–308.
  34. [34] Y. Ding, Q. Zhang, T. Yuan, and F. Yang, (2018) “Effect of input variables on cooling load prediction accuracy of an office building" Applied Thermal Engineering 128: 225–234.
  35. [35] A. Moradzadeh, A. Mansour-Saatloo, B. Mohammadi-Ivatloo, and A. Anvari-Moghaddam, (2020) “Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings" Applied Sciences 10: 3829. DOI: 10.3390/app10113829.
  36. [36] A. Moradzadeh, S. Zeinal-Kheiri, B. MohammadiIvatloo, M. Abapour, and A. Anvari-Moghaddam. “Support vector machine-assisted improvement residential load disaggregation”. In: IEEE, 2020, 1–6.
  37. [37] X. J. Luo, L. O. Oyedele, A. O. Ajayi, and O. O. Akinade, (2020) “Comparative study of machine learningbased multi-objective prediction framework for multiple building energy loads" Sustainable Cities and Society 61: 102283.
  38. [38] A. Moradzadeh, S. Zakeri, M. Shoaran, B. Mohammadi-Ivatloo, and F. Mohammadi, (2020) “Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms" Sustainability 12: 7076.
  39. [39] Y. Ding, H. Su, X. Kong, and Z. Zhang, (2020) “Ultrashort-term building cooling load prediction model based on feature set construction and ensemble machine learning" IEEE Access 8: 178733–178745.
  40. [40] C. Fan, F. Xiao, and Y. Zhao, (2017) “A short-term building cooling load prediction method using deep learning algorithms" Applied energy 195: 222–233. DOI: 10.1016/j.apenergy.2017.03.064.
  41. [41] C. Fan, J. Wang, W. Gang, and S. Li, (2019) “Assessment of deep recurrent neural network-based strategies for short-term building energy predictions" Applied energy 236: 700–710.
  42. [42] Z. Wang, T. Hong, and M. A. Piette, (2019) “Data fusion in predicting internal heat gains for office buildings through a deep learning approach" Applied energy 240: 386–398.
  43. [43] J. Song, G. Xue, X. Pan, Y. Ma, and H. Li, (2020) “Hourly heat load prediction model based on temporal convolutional neural network" IEEE Access 8: 16726–16741.
  44. [44] N. M. Kadhim, H. A. Mohammed, S. N. Radhawi, A. M. Jabur, R. B. Gottraan, M. M. Abdulridha, W. M. Kadhim, and Z. Q. Mohammed, (2023) “Investigation of the next generation science standards including in the science book according to E-learn: Analytical study" Tamjeed Journal of Healthcare Engineering and Science Technology 1: 30–35.
  45. [45] A. A. Hamad, M. M. Abdulridha, N. M. Kadhim, S. Pushparaj, R. Meenakshi, and A. M. Ibrahim, (2022) “Learning methods of business intelligence and group related diagnostics on patient management by using artificial dynamic system" Journal of Nanomaterials 2022: 1–8.
  46. [46] I. D. Mienye, Y. Sun, and Z. Wang, (2019) “Prediction performance of improved decision tree-based algorithms: a review" Procedia Manufacturing 35: 698–703.
  47. [47] A. Yezioro, B. Dong, and F. Leite, (2008) “An applied artificial intelligence approach towards assessing building performance simulation tools" Energy and buildings 40: 612–620. DOI: 10.1016/j.enbuild.2007.04.014.
  48. [48] M. Alsaffar, A. A. Hamad, A. Alshammari, G. Alshammari, T. S. Almurayziq, M. S. Mohammed, and W. Enbeyle, (2021) “Network management system for IoT based on dynamic systems" Computational and Mathematical Methods in Medicine 2021:
  49. [49] F. T. JASIM, M. F. JWAID, and M. Karthick, (2023) “Artificial intelligence innovation and human resource recruitment" Tamjeed Journal of Healthcare Engineering and Science Technology 1: 20–29.
  50. [50] D. Geysen, O. D. Somer, C. Johansson, J. Brage, and D. Vanhoudt, (2018) “Operational thermal load forecasting in district heating networks using machine learning and expert advice" Energy and Buildings 162: 144–153.
  51. [51] B. Cui, C. Fan, J. Munk, N. Mao, F. Xiao, J. Dong, and T. Kuruganti, (2019) “A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses" Applied energy 236: 101–116.
  52. [52] R. Wang, S. Lu, and W. Feng, (2020) “A novel improved model for building energy consumption prediction based on model integration" Applied Energy 262: 114561.
  53. [53] S. Paudel, M. Elmtiri, W. L. Kling, O. L. Corre, and B. Lacarrière, (2014) “Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network" Energy and Buildings 70: 81–93.
  54. [54] B. Sadaghat, A. J. Khiavi, B. Naeim, E. Khajavi, A. R. T. Khanghah, and H. Sadaghat, (2023) “The utilization of a Naïve Bayes model for predicting the energy consumption of buildings" Journal of Artificial Intelligence and System Modelling 1:
  55. [55] H. Zhong, J. Wang, H. Jia, Y. Mu, and S. Lv, (2019) “Vector field-based support vector regression for building energy consumption prediction" Applied Energy 242: 403–414.
  56. [56] T. Ahmad and H. Chen, (2018) “Short and mediumterm forecasting of cooling and heating load demand in building environment with data-mining based approaches" Energy and Buildings 166: 460–476.
  57. [57] Z. Xuan, Z. Xuehui, L. Liequan, F. Zubing, Y. Junwei, and P. Dongmei, (2019) “Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building" Journal of Building Engineering 21: 64–73.
  58. [58] C. Fan and Y. Ding, (2019) “Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model" Energy and Buildings 197: 7–17.
  59. [59] G. Zhou, H. Moayedi, M. Bahiraei, and Z. Lyu, (2020) “Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings" Journal of Cleaner Production 254: 120082.
  60. [60] W. Pessenlehner and A. Mahdavi. Building morphology, transparence, and energy performance. Citeseer, 2003.
  61. [61] B. A. Omran, Q. Chen, and R. Jin, (2016) “Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete" Journal of Computing in Civil Engineering 30: 04016029.
  62. [62] S. Chithra, S. R. R. S. Kumar, K. Chinnaraju, and F. A. Ashmita, (2016) “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks" Construction and Building Materials 114: 528–535. DOI: 10.1016/j.conbuildmat.2016.03.214.
  63. [63] M. Ebden, (2015) “Gaussian processes: A quick introduction" arXiv preprint arXiv:1505.02965:
  64. [64] M.-Y. Cheng, C.-C. Huang, and A. F. V. Roy, (2013) “Predicting project success in construction using an evolutionary Gaussian process inference model" Journal of Civil Engineering and Management 19: S202–S211.
  65. [65] E. Trojovská, M. Dehghani, and P. Trojovský, (2022) “Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm" IEEE Access 10: 49445–49473.
  66. [66] S. Sarjamei, M. S. Massoudi, and M. E. Sarafraz, (2021) “Gold rush optimization algorithm" Iran Univ. Sci. Technol 11: 291–327.


    



 

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