Elbeltagi, E., Wefki, H., Khallaf, R. A sustainable building optimization model for early stage design. building 13(1), 74 (2022).
Das, S., Swetapadma, A., Panigrahi, C. and Abdelaziz, A.Y. Improved methodology for approximation of heating and cooling loads of urban buildings for improved energy performance. Power Calculation System 48(4–5), 436–446 (2020).
El-Sayed, AHA, Khalil, A. & Yehia, M. Modelling alternative scenarios for Egypt's 2050 energy mix based on LEAP analysis. energy 266126615 (2023).
Seyedzadeh, S., Rahimian, F.P., Glesk, I., Roper, M. Machine learning for estimating building energy consumption and performance: a review. Visual. English. 61–20 (2018).
IEA International Energy Agency. Major World Energy Statistics (IEA, 2015).
Google Academic
Swan, LG & Ugursal, VI “Modelling end-use energy consumption in the residential sector: a review of modelling methods” Regeneration. Sustaining. EnergyRev. 131819–1835 (2009).
Mui, KW, Satheesan, MK & Wong, LT Building cooling energy consumption prediction using a hybrid simulation approach: generalization beyond training scope. Energy build. 276112502 (2022).
Al-Shargabi, AA, Almhafdy, A., Ibrahim, DM, Alghieth, M., Chiclana, F. Building energy consumption prediction models based on building characteristics: research trends, classification methods and performance measurements. J. Build. Eng. 54104577 (2022).
Ilbeigi, M., Ghomeishi, M., Dehghanbanadaki, A. Prediction and optimization of energy consumption in office buildings using artificial neural networks and genetic algorithms. Sustain. Cities Society 61102325 (2020).
Amasyali, K. & El-Gohary, N. M. A review of data-driven building energy consumption forecasting studies. Regeneration. Sustaining. EnergyRev. 811192–1205 (2018).
Mansour, DM & Ebid, AM Modelling heat transfer in large scale concrete foundations using 3D-FDM. Civilization.English. J. 9(10), 2430–2444 (2023).
Mansour, DM & Ebid, AM Predicting the thermal behavior of mass concrete elements using a 3D finite difference model. Asian Journal of Civil Engineering twenty five(2), 1601–1611 (2024).
Elbeltagi, E., Wefki, H., Abdrabou, S., Dawood, M., Ramzy, A. A visualized strategy for predicting building energy consumption at the early design stage using parametric analysis. J. Build. Eng. 13127–136 (2017).
Welle, B., Haymaker, J., Rogers, Z. ThermalOpt: An automated BIM-based multi-disciplinary thermal simulation methodology for use in optimization environments. Build. Simulate. Four293–313 (2013).
Biswas, MR, Robinson, MD & Fumo, N. Predicting energy consumption in residential buildings: A neural network approach. energy 11784–92 (2016).
Castelli, M., Trujillo, L., Vanneschi, L., Popovič, A. Predicting residential energy performance: a genetic programming approach. Energy build. 10267–74. https://doi.org/10.1016/j.enbuild.2015.05.01310.1016/j.enbuild.2015.05.013 (2015).
Tahmassebi, A. & Gandomi, A.H. Building energy consumption forecasting using multi-objective genetic programming. measurement 118164–171 (2018).
Matt Dowth, MA etc Building electrical energy consumption forecast analysis using traditional and artificial intelligence methods: a review. Regeneration. Sustaining. EnergyRev. 701108–1118. https://doi.org/10.1016/j.rser.2016.12.015 (2017).
Yazici, I., Beyca, O.F., Delen, D. Deep learning based short-term electricity load forecasting: application to a real case. Engineering, Applications, and Artificial Intelligence 109104645 (2022).
Jamei, M. etc Estimation of density of hybrid nanofluids for thermal energy applications: Application of non-parametric and evolutionary polynomial regression data intelligence techniques. measurement 189110524 (2022).
Yin, Z.-Y. & Jin, Y.-F. Optimization-based evolutionary polynomial regression. Practice, Optimization, Theory, Geotechnical Engineering https://doi.org/10.1007/978-981-13-3408-5_5 (2019).
Khan, S.U. etc Towards intelligent building energy management: An AI-based framework for electricity consumption and generation forecasting. Energy build. 279112705 (2023).
Runge, J. & Saloux, E. Comparison of predictive and forecasting artificial intelligence models for estimating future energy demand in district heating systems. energy 269126661 (2023).
Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., Ajayi, S. Residential energy consumption prediction using deep learning and other machine learning techniques. J. Build. Eng. 45103406 (2022).
Yang, W. etc A composite deep learning load forecasting model for single-household residential users considering multi-time scale electricity consumption behavior. Applied Energy 307118197 (2022).
Li, X. & Yao, R. Modelling heating and cooling energy demand of building stock using a hybrid approach. Energy build. 235110740 (2021).
Zou, Y., Xiang, K., Zhan, Q. & Li, Z. A simulation-based method for predicting the life cycle energy performance of residential buildings in different climatic zones of China. Construction. Environment. 193107663 (2021).
D'Amico, A., Ciulla, G., Traverso, M., Brano, VL, Palumbo, E. Artificial neural networks for assessing the energy and environmental performance of buildings: an Italian case study. J. Crean Productions. 239117993 (2019).
Mohammadi, M., Talebpour, F., Safaee, E., Ghadimi, N., Abedinia, O. Load forecasting for small buildings based on a hybrid forecasting engine. Neural processes. Lett. 48329–351 (2018).
Ullah, I., Ahmad, R., Kim, D. Prediction mechanism for residential energy consumption using hidden Markov model. energy 11(2), 358 (2018).
Fayaz, M. & Kim, D. Energy consumption prediction method based on deep extreme learning machine and comparative analysis of residential buildings. electronics 7(10), 222 (2018).
Ascione, F., Bianco, N., De Stasio, C., Mauro, GM & Vanoli, GP Prediction of energy performance and renovation scenarios for any member of a building category with artificial neural networks: a new approach. energy 118999–1017 (2017).
Mocanu, E., Nguyen, P. H., Gibescu, M., Kling, W. L. Deep learning for estimating building energy consumption. Sustainable energy grid network. 691–99 (2016).
Samuelson, H., Claussnitzer, S., Goyal, A., Chen, Y., Romo-Castillo, A. Parametric energy simulation in early design: a high-rise residential building in an urban environment. Construction. Environment. 10119–31 (2016).
Fumo, N. & Biswas, M. R. Regression analysis for prediction of residential energy consumption. Regeneration. Sustaining. EnergyRev. 47332–343 (2015).
Fan, C., Xiao, F., Wang, S. Development of prediction model for next-day building energy consumption and peak electricity demand using data mining techniques. Applied Energy 1271–10 (2014).
Elbeltagi, E. & Wefki, H. Prediction of residential energy consumption using ANN with parametric modeling. Energy Officer 72534–2545 (2021).
Elhabyb, K., Baina, A., Bellafkih, K., Deifalla, A.F. Machine learning algorithms for predicting energy consumption in educational facilities. International Energy Research Association 20241–19. https://doi.org/10.1155/2024/6812425 (2014).
