Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm
- Authors
- Aslam, M.S.; Ghazal, T.M.; Fatima, A.; Said, R.A.; Abbas, S.; Khan, M.A.; Siddiqui, S.Y.; Ahmad, M.
- Issue Date
- Aug-2021
- Publisher
- TSI PRESS
- Keywords
- Gradient descent optimization; Heating-load prediction; Machine learning
- Citation
- Intelligent Automation and Soft Computing, v.30, no.3, pp.881 - 888
- Journal Title
- Intelligent Automation and Soft Computing
- Volume
- 30
- Number
- 3
- Start Page
- 881
- End Page
- 888
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82092
- DOI
- 10.32604/iasc.2021.017920
- ISSN
- 1079-8587
- Abstract
- The 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.
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