Multi-Objective Building Retrofitting Utilizing Evolutionary Algorithms and Machine Learning Models

Authors

  • Ashuvendra Singh Assistant Professor, SSchool of Civil Engineering, Dev Bhoomi Uttarakhand University, Uttarakhand, India
  • Beemkumar N. Professor, Department of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
  • Tushar Mehrotra Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Shish Dubey Assistant professor, School of Computer Science & System, JAIPUR NAITONAL UNIVERSITY, JAIPUR, India

Keywords:

Retrofitting building, energy consumption, multi evolutionary genetic-optimized artificial neural network algorithm (MEGANN)

Abstract

The retrofitting of existing buildings provides an enormous opportunity for increasing tenant wellbeing and comfort while lowering worldwide greenhouse gas emissions and energy usage. This is regarded as one of the primary techniques for obtaining environmental sustainability through construction at a less cost and with the highest rate of adoption. Although a large selection of retrofit methodologies is gladly accessible, strategies for identifying the most appropriate combination of retrofit operations for specific projects remain a serious technical problem. This study provides a novel Multi Evolutionary Genetic-optimized Artificial Neural Network algorithm (MEGANN) to quantitatively evaluate technological possibilities in a building retrofitting project. This research offers the speed of estimation with the power of Multi-objective evolutionary optimization and machine learning algorithms. The analysis begins with the individualized optimization of objective functions with a focus on the properties and efficiency of the building, including energy consumption and retrofit cost. We evaluate our approach by comparing it to conventional procedures. The results are superior to those obtained by any other currently available techniques.

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Published

11.07.2023

How to Cite

Singh, A. ., N., B. ., Mehrotra, T. ., & Dubey, S. . (2023). Multi-Objective Building Retrofitting Utilizing Evolutionary Algorithms and Machine Learning Models. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 117–122. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3029

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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