DOI: https://doi.org/10.61435/ijred.2024.60153

Energy efficient design of rural prefabricated buildings based on ANN and NSGA-II

1School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang, 471000, China

2Luoyang Xiaozhaidi Construction Technology Co., Ltd, Luoyang, 471000, Henan, China

Received: 24 Feb 2024; Revised: 17 Jul 2024; Accepted: 15 Aug 2024; Available online: 29 Aug 2024; Published: 1 Sep 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
The growing concern about global climate change and the rapid development of rural areas highlight the need for energy efficient building design. This study aims to establish a multi-objective optimization model based on artificial neural network (ANN) and non-dominated sorting Genetic algorithm II (NSGA-II) to optimize the energy consumption of rural prefabricated buildings. Firstly, ANN and simulation technology are used to build building models and predict building energy consumption. Then, NSGA-II algorithm was used to optimize the energy consumption and material selection of the building, and the best prefabricated building scheme was obtained. The experimental results show that the optimization efficiency of the model is about 95%, which is better than the traditional method. Specifically, compared with the NSGA-II algorithm, the model reduces energy consumption by 16.7%, operating costs by 20.0%, and carbon emissions by 20.0%. When the cost optimization, energy consumption optimization and carbon emission optimization are difficult to balance, the average optimization efficiency of the research design method is about 90% when the cost optimization rate is low, and the other optimization rates are about 85% when the cost optimization rate rises to 50%. When the cost optimization reaches the maximum, the optimization rate remains at about 80%. These results show that the proposed model is robust and efficient. This study provides a comprehensive framework for designing sustainable and energy efficient rural prefabricated buildings that can help reduce energy consumption and environmental impact. It has positive significance in the sustainable development of rural economy and provides a new way of thinking for rural construction.
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Keywords: Rural areas; Artificial Neural Network; Non-dominated Sorting Genetic Algorithm II; Prefabricated buildings; Energy optimization

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Section: Original Research Article
Language : EN
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