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References
Abdollahzadeh, M., Khosravi, M., Hajipour Khire Masjidi, B., et al. (2022). Estimating the density of deep eutectic solvents applying supervised machine learning techniques. Scientific Reports. https://doi.org/10.1038/s41598-022-08842-5
Ahmadi, S. A., & Ali, P. (2022). Sustainable portfolio optimization model using promethee ranking: A case study of palm oil buyer companies. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2022/8935213
Ahmadi, S., Motie, M., & Soltanmohammadi, R. (2020). Proposing a modified mechanism for determination of hydrocarbons dynamic viscosity, using artificial neural network. Petroleum Science and Technology, 38, 699–705. https://doi.org/10.1080/10916466.2020.1780256
Amini, S., Bahramara, S., Golpîra, H., et al. (2022). Techno-economic analysis of renewable-energy-based micro-grids considering incentive policies. Energies. https://doi.org/10.3390/en15218285
Ashrafi, R., Azarbayjani, M., Cox, R., et al. (2019). Assessing the performance of UFAD system in an office building located in various climate zones. In Proceedings of the Symposium on Simulation for Architecture
Cen, C., Kenli, L., Aijia, O., et al. (2017). GPU-accelerated parallel hierarchical extreme learning machine on flink for big data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 2740–2753. https://doi.org/10.1109/TSMC.2017.2690673
Cen, C., Kenli, L., Aijia, O., et al. (2018). GFlink: An in-memory computing architecture on heterogeneous CPU-GPU clusters for big data. IEEE Transactions on Parallel and Distributed Systems, 29, 1275–1288. https://doi.org/10.1109/TPDS.2018.2794343
Chen, C., et al. (2020). Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks. ACM Transactions on Knowledge Discovery from Data, 14, 1–23. https://doi.org/10.1145/3385414
Chen, C., et al. (2021a). Hierarchical graph neural networks for few-shot learning. IEEE Transactions on Circuits and Systems for Video Technology, 32, 240–252. https://doi.org/10.1109/TCSVT.2021.3058098
Chen, J., et al. (2016). A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TPDS.2016.2603511
Chen, J., et al. (2018). A bi-layered parallel training architecture for large-scale convolutional neural networks. IEEE Transactions on Parallel and Distributed Systems. https://doi.org/10.1109/TPDS.2018.2877359
Chen, J., et al. (2021b). Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3446342
Chen, N., Masoud, V., & Peivandizadeh, A. (2022). Forecasting directions dates and causes of future technological revolutions concerning the Growth of Human Capital. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2022/2494916
Dehghani, F., Larijani, A. (2023a). A machine learning-jaya algorithm (Ml-Ijaya) approach for rapid optimization using high performance computing. https://doi.org/10.2139/ssrn.4423338
Dehghani, F., Larijani, A. (2023b). An algorithm for predicting stock market’s index based on MID algorithm and neural network. https://doi.org/10.2139/ssrn.4448033
Dinmohammadi, F., Yuxuan, H., & Mahmood, S. (2023). Predicting energy consumption in residential buildings using advanced machine learning algorithms. Energies. https://doi.org/10.3390/en16093748
Duan, M., et al. (2017). A parallel multiclassification algorithm for big data using an extreme learning machine. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2017.2654357
Duan, M., et al. (2020). A novel multi-task tensor correlation neural network for facial attribute prediction. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3418285
El-Sisi, A., et al. (2022). Failure behavior of composite bolted joints. CivilEng. https://doi.org/10.3390/civileng3040060
Fallah, A. M., Ghafourian, E., Shahzamani Sichani, L., et al. (2023). Novel neural network optimized by electrostatic discharge algorithm for modification of buildings energy performance. Sustainability, 15, 2884. https://doi.org/10.3390/su15042884
Fatourehchi, D., & Zarghami, E. (2020). Social sustainability assessment framework for managing sustainable construction in residential buildings. Journal of Building Engineering. https://doi.org/10.1016/j.jobe.2020.101761
Gao, X., et al. (2020). Forecasting the heat load of residential buildings with heat metering based on CEEMDAN-SVR. Energies, 13, 6079. https://doi.org/10.3390/en13226079
Golpîra, H., Amini, S., Atarodi, A., & Bevrani, H. (2022). A data-driven inertia adequacy-based approach for sustainable expansion planning in distributed generations-penetrated power grids. IET Generation, Transmission & Distribution, 16, 4614–4629. https://doi.org/10.1049/gtd2.12626
Hanc, M., McAndrew, C., & Ucci, M. (2019). Conceptual approaches to wellbeing in buildings: A scoping review. Building Research & Information, 47, 767–783. https://doi.org/10.1080/09613218.2018.1513695
Hashemi, A., Jang, J., & Javad, B. (2023). A machine learning-based surrogate finite element model for estimating dynamic response of mechanical systems. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3282453
Hossain, J., et al. (2023). A review on optimal energy management in commercial buildings. Energies. https://doi.org/10.3390/en16041609
IEA (2011) World energy outlook 2011 executive summary. International Energy Agency
Ilbeigi, M., Bairaktarova, D., & Morteza, A. (2023). Gamification in construction engineering education: a scoping review. Journal of Civil Engineering Education. https://doi.org/10.1061/(ASCE)EI.2643-9115.000007
Iraji, S., Soltanmohammadi, R., Matheus, G. F., et al. (2023). Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data. https://doi.org/10.2139/ssrn.4486002
Jang, J., Han, J., & Seung-Bok, L. (2022). Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks. Energy Buildings. https://doi.org/10.1016/j.enbuild.2021.111647
Kang, X., et al. (2022). A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings. Energy Buildings. https://doi.org/10.1016/j.enbuild.2022.112478
Karki, V., Mostafa, R., Gopalakrishnan, B., & Johnson, D. R. (2023). Determination of effectiveness of energy management system in buildings. Energy Engineering, 120, 561–586.
Kashani, S. A., Soleimani, A., Khosravi, A., & Mirsalim, M. (2023). State-of-the-art research on wireless charging of electric vehicles using solar energy. Energies. https://doi.org/10.3390/en16010282
Kaveh, A., et al. (2021). Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders. Acta Mechanica. https://doi.org/10.1007/s00707-020-02878-2
Kaveh, A., & Khalegi, A. (1998). Prediction of strength for concrete specimens using artificial neural networks. In B. H. V Topping (Ed), Advances in Engineering Computational Technology (pp. 165–171). UK: Civil-Comp Press. https://doi.org/10.1016/S0141-0296(03)0004-X
Kaveh, A., Kavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256. https://doi.org/10.1016/j.istruc.2023.03.178
Khajavi, H., & Amir, R. (2023). Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms. Energy, 272, 127069. https://doi.org/10.1016/j.energy.2023.127069
Khorasgani, A. M., Villalobos, M. H., & Eskandar, G. A. (2023). Sustaining historic cities: An approach using the ideas of landscape and place. ISVS e-Journal, 10, 320–332.
Khoshnevisan, B., et al. (2014). Development of an intelligent system based on ANFIS for predicting wheat grain yield based on energy inputs. Information Processing in Agriculture, 1, 14–22. https://doi.org/10.1016/j.inpa.2014.04.001
Kim, Y., et al. (2023). Load prediction algorithm applied with indoor environment sensing in university buildings. Energies. https://doi.org/10.3390/en16020999
Li, C., & Youming, C. (2023). A multi-factor optimization method based on thermal comfort for building energy performance with natural ventilation. Energy and Buildings, 285, 112893. https://doi.org/10.1016/j.enbuild.2023.112893
Li, K., Tang, X., & Keqin, L. (2013). Energy-efficient stochastic task scheduling on heterogeneous computing systems. IEEE Transactions on Parallel and Distributed Systems, 25, 2867–2876. https://doi.org/10.1109/TPDS.2013.270
Li, Q., et al. (2009). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86, 2249–2256. https://doi.org/10.1016/j.apenergy.2008.11.035
Maulud, D., & Adnan, M. A. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technolology Trends, 1(4), 140–147. https://doi.org/10.38094/jastt1457
Maydanchi, M., Ziaei, A., Basiri, M., et al. (2023). Comparative Study of decision tree, adaboost, random forest, Naïve Bayes, KNN, and perceptron for heart disease prediction. SoutheastCon. https://doi.org/10.1109/SoutheastCon51012.2023.10115189
Moayedi, H., & Mosavi, A. (2021). Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings. Sustainability. https://doi.org/10.3390/su13063198
Molina-Solana, M., et al. (2017). Data science for building energy management: A review. Renewable and Sustainable Energy Reviews, 70, 598–609. https://doi.org/10.1016/j.rser.2016.11.132
Moshtaghi Largani, S, Lee, S (2023) Efficient sampling for big provenance. https://doi.org/10.1145/3543873.3587556
Naseri, H., Jahanbakhsh, H., Foomajd, A., et al. (2022). A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying Whale Optimization Algorithm and random forest regression. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2022.2147672
Nhu, V.-H., et al. (2020c). Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph17082749
Peivandizadeh, A., & Molavi, B. (2023). Compatible authentication and key agreement protocol for low power and lossy network in iot environment. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4454407
Runge, J., & Radu, Z. (2019). Forecasting energy use in buildings using artificial neural networks: A review. Energies. https://doi.org/10.3390/en12173254
Sasani, F., Mousa, R., Karkehabadi, A., et al. (2023). TM-vector: A novel forecasting approach for market stock movement with a rich representation of twitter and market data. arXiv Preprint. https://doi.org/10.48550/arXiv.2304.02094
Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13, 1819–1835. https://doi.org/10.1016/j.rser.2008.09.033
Tang, X., et al. (2012). A hierarchical reliability-driven scheduling algorithm in grid systems. Journal of Parallel and Distributed Computing, 72, 525–535. https://doi.org/10.1016/j.jpdc.2011.12.004
Tran, D.-H., Duc-Long, L., & Jui-Sheng, C. (2020). Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings. Energy. https://doi.org/10.1016/j.energy.2019.116552
Wang, R. D., et al. (2021b). A system boundary-based critical review on crane selection in building construction. Automation Construction. https://doi.org/10.1016/j.autcon.2020.103520
Wangdong, Y., Kenli, L., Zeyao, M., & Keqin, L. (2015). Performance optimization using partitioned SpMV on GPUs and multicore CPUs. IEEE Transactions on Computers, 64, 2623–2636. https://doi.org/10.1109/TC.2014.2366731
Wu, L., Kaiser, G., Solomon, D., et al. (2012). Improving efficiency and reliability of building systems using machine learning and automated online evaluation. IEEE Long Isl Systems, Applications, and Technology Conference. https://doi.org/10.1109/LISAT.2012.6223192
Yang J, Rivard H, Zmeureanu R (2005) Building energy prediction with adaptive artificial neural networks. Ninth International IBPSA Conference Montréal
Yu, Z., Haghighat, F., Fung, B. C. M., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy Building, 42, 1637–1646. https://doi.org/10.1016/j.enbuild.2010.04.006
Zhai, Z. J., & Helman, J. M. (2019). Implications of climate changes to building energy and design. Sustain Cities Soc 511–519. https://doi.org/10.1016/j.scs.2018.10.043
Zhao, J., Yaoqi, D., & Xiaojuan, L. (2018). Uncertainty analysis of weather forecast data for cooling load forecasting based on the Monte Carlo method. Energies. https://doi.org/10.3390/en11071900
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Mehdizadeh Khorrami, B., Soleimani, A., Pinnarelli, A. et al. Correction: Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators. Asian J Civ Eng 25, 2349–2351 (2024). https://doi.org/10.1007/s42107-023-00865-1
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DOI: https://doi.org/10.1007/s42107-023-00865-1