Fan Li1, Lu Li2This email address is being protected from spambots. You need JavaScript enabled to view it., and Fucai You3
1College of Energy and Power, Shenyang Institute of Engineering, Shenyang 110136, Liaoning, China
2Dean’s Office, Anhui University of Finance & Economics, Bengbu 233030, Anhui, China
3Academic Affairs Office, Shenyang Intitute of Engineering, Shenyang 110136, Liaoning, China
Received: January 30, 2024 Accepted: July 8, 2024 Publication Date: September 8, 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.
Estimating the cooling load is essential for cooling systems’ energy efficiency, affecting chiller optimization and air conditioning control. Regression analysis and other conventional techniques are insufficient, but AI and ML have superior capabilities. AI and ML models are able to independently capture complicated patterns, adapt to changing settings, and scale with data efficiently to anticipate cooling loads that are impacted by variables such as weather, building materials, and occupancy. The dynamic, responsive predictions that result from this adaptability improve energy optimization and effective building management. This study delves deep into the intricate landscape of cooling load systems, where energy optimization complexities present challenges requiring thorough investigation and innovative problemsolving approaches. The Least Square Support Vector Regression (LSSVR) model is seamlessly combined with 2 meta-heuristic methods, the Dandelion Optimizer (DO) and the Electric Charged Particles Optimisation (ECPO), for improving precision and correctness. These algorithms use cooling load data that has undergone extensive validation through previous stability tests. 3 different models, LSDO, LSECH, and an independent LSSVR, are introduced in the study, each of which provides insightful information for accurate cooling load prediction. With an impressive R2 of 0.990 and an incredibly low RMSE of 0.927 , the LSECH stands out as a standout performer. These outstanding outcomes demonstrate the LSECH model’s ability to accurately predict cooling load outcomes, emphasizing its potential for use in actual building management scenarios.
Keywords: Building energy; Cooling load; Least Square Support Vector Regression; Dandelion optimizer; Electric Charged Particles optimization
[1] 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.
[2] R. Chaganti, F. Rustam, T. Daghriri, I. d. l. T. Díez, J. L. V. Mazón, C. L. Rodríguez, and I. Ashraf, (2022) “Building heating and cooling load prediction using ensemble machine learning model" Sensors 22(19): 7692. DOI: 10.3390/s22197692.
[3] E. Gago, T. Muneer, M. Knez, and H. Köster, (2015) “Natural light controls and guides in buildings. Energy saving for electrical lighting, reduction of cooling load" Renewable and Sustainable Energy Reviews 41: 1– 13. DOI: 10.1016/j.rser.2014.08.002.
[4] Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, (2009) “Applying support vector machine to predict hourly cooling load in the building" Applied Energy 86(10): 2249–2256. DOI: 10.1016/j.apenergy.2008.11.035.
[5] S. S. Roy, R. Roy, and V. E. Balas, (2018) “Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM" Renewable and Sustainable Energy Reviews 82: 4256–4268. DOI: 10.1016/j.rser.2017.05.249.
[6] S. Afzal, B. M. Ziapour, A. Shokri, H. Shakibi, and B. Sobhani, (2023) “Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms" Energy 282: 128446. DOI: 10.1016/j.energy.2023.128446.
[8] H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, (2016) “Machine learning basics" Deep learning: 98–164.
[9] G. G. Tejani, B. Sadaghat, and S. Kumar, (2023) “Predict the maximum dry density of soil based on individual and hybrid methods of machine learning" Advances in engineering and intelligence systems 2(03): 98–109. DOI: 10.22034/aeis.2023.414188.1129.
[10] F. Masoumi, S. Najjar-Ghabel, A. Safarzadeh, and B. Sadaghat, (2020) “Automatic calibration of the groundwater simulation model with high parameter dimensionality using sequential uncertainty fitting approach" Water Supply 20(8): 3487–3501. DOI: 10.2166/ws.2020.241.
[11] M. R. Akbarzadeh, H. Ghafourian, A. Anvari, R. Pourhanasa, and M. L. Nehdi, (2023) “Estimating compressive strength of concrete using neural electromagnetic field optimization" Materials 16(11): 4200. DOI: 10.3390/ma16114200.
[12] Y. Ding, Q. Zhang, and T. Yuan, (2017) “Research on short-term and ultra-short-term cooling load prediction models for office buildings" Energy and Buildings 154: 254–267. DOI: 10.1016/j.enbuild.2017.08.077.
[13] H. A. Shah, Q. Yuan, U. Akmal, S. A. Shah, A. Salmi, Y. A. Awad, L. A. Shah, Y. Iftikhar, M. H. Javed, and M. I. Khan, (2022) “Application of machine learning techniques for predicting compressive, splitting tensile, and flexural strengths of concrete with metakaolin" Materials 15(15): 5435. DOI: 10.3390/ma15155435.
[14] M. Taki and A. Rohani, (2022) “Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation" Case Studies in Thermal Engineering 31: 101823. DOI: 10.1016/j.csite.2022.101823.
[15] H. Sha, M. Moujahed, and D. Qi, (2021) “Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings" Energy and Buildings 242: 110980. DOI: 10.1016/j.enbuild.2021.110980.
[16] A. E. Ben-Nakhi and M. A. Mahmoud, (2004) “Cooling load prediction for buildings using general regression neural networks" Energy Conversion and Management 45(13-14): 2127–2141. DOI: 10.1016/j.enconman.2003.10.009.
[17] Z. Gao, J. Yu, A. Zhao, Q. Hu, and S. Yang, (2022) “A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine" Energy 238: 122073. DOI: 10.1016/j.energy.2021.122073.
[18] Y. Gao, Y. Hang, and M. Yang, (2021) “A cooling load prediction method using improved CEEMDAN and Markov Chains correction" Journal of Building Engineering 42: 103041. DOI: 10.1016/j.jobe.2021.103041.
[19] 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(11): 3829. DOI: 10.3390/app10113829.
[20] S. S. Kwok and E. W. Lee, (2011) “A study of the importance of occupancy to building cooling load in prediction by intelligent approach" Energy Conversion and Management 52(7): 2555–2564. DOI: 10.1016/j.enconman. 2011.02.002.
[21] C. Fan, Y. Liao, G. Zhou, X. Zhou, and Y. Ding, (2020) “Improving cooling load prediction reliability for HVAC system using Monte-Carlo simulation to deal with uncertainties in input variables" Energy and Buildings 226: 110372. DOI: 10.1016/j.enbuild.2020.110372.
[22] J. A. Suykens and J. Vandewalle, (2000) “Recurrent least squares support vector machines" IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 47(7): 1109–1114. DOI: 10.1109/81.855471.
[23] O. Kisi, (2015) “Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering" Water resources management 29: 5109–5127. DOI: 10.1007/ s11269-015-1107-7.
[24] M. Kumar and I. Kar, (2009) “Non-linear HVAC computations using least square support vector machines" Energy Conversion and Management 50(6): 1411–1418. DOI: 10.1016/j.enconman.2009.03.009.
[25] M. M. Ghiasi, A. Shahdi, P. Barati, and M. Arabloo, (2014) “Robust modeling approach for estimation of compressibility factor in retrograde gas condensate systems" Industrial & Engineering Chemistry Research 53(32): 12872–12887. DOI: 10.1021/ie404269b.
[26] N. M. Mahmoodi, M. Arabloo, and J. Abdi, (2014) “Laccase immobilized manganese ferrite nanoparticle: Synthesis and LSSVM intelligent modeling of decolorization" Water research 67: 216–226. DOI: 10.1016/j.watres.2014.09.011.
[27] O. Kisi and K. S. Parmar, (2016) “Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution" Journal of Hydrology 534: 104–112. DOI: 10.1016/j.jhydrol.2015.12.014.
[28] D. Moreno-Salinas, D. Chaos, E. Besada-Portas, J. A. López-Orozco, J. M. de la Cruz, and J. Aranda, (2013) “Semiphysical Modelling of the Nonlinear Dynamics of a Surface Craft with LS-SVM" Mathematical Problems in Engineering 2013(1): 890120. DOI: 10.1155/2013/ 890120.
[29] S.-G. Cao, Y.-B. Liu, and Y.-P. Wang, (2008) “A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM" Journal of China University of Mining and Technology 18(2): 172–176. DOI: 10.1016/S1006-1266(08)60037-1.
[30] R. Fletcher. Practical methods of optimization. John Wiley & Sons, 2000.
[31] S. Zhao, T. Zhang, S. Ma, and M. Chen, (2022) “Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications" Engineering Applications of Artificial Intelligence 114: 105075. DOI: 10.1016/j.engappai.2022.105075.
[32] H. Bouchekara, (2021) “Electric Charged Particles Optimization and its application to the optimal design of a circular antenna array" Artificial Intelligence Review 54(3): 1767–1802. DOI: 10.1007/s10462-020-09890-x.
We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.
