Aslam, Muhammad ShoukatGhazal, Taher M.Fatima, AreejSaid, Raed A.Abbas, SagheerKhan, Muhammad AdnanSiddiqui, Shahan YaminAhmad, Munir2021-09-122021-09-12© 20212021Aslam, M. S., Ghazal, T. M., Fatima, A., Said, R. A., Abbas, S., Khan, M. A., . . . Ahmad, M. (2021). Energy-efficiency model for residential buildings using supervised machine learning algorithm. Intelligent Automation and Soft Computing, 30(3), 881-888. https://www.techscience.com/iasc/v30n3/44089Volume 30, Issue 3https://doi.org/10.32604/iasc.2021.017920http://hdl.handle.net/20.500.12519/438The real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appro-priate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent optimization (E2B-GDO). This model can predict a building’s heating-load conservation based on a building energy performance dataset. The input layer includes area (distribution of the glazing area, wall area, and surface area), relative density, and overall elevation. The proposed E2B-GDO model achieved an accuracy of 99.98% for training and 98.00% for validation. © 2021, Tech Science Press. All rights reserved.enCreative Commons Attribution 4.0 International LicenseGradient descent optimizationHeating-load predictionMachine learningArticleCopyright : © 2021, Tech Science Press. All rights reserved.