Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm

Authors

  • Chaoqin Bai School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang, 471000, China
  • Junrui Liu School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang, 471000, China

DOI:

https://doi.org/10.13052/spee1048-5236.4328

Keywords:

Carbon emissions, building energy consumption, internet of things, cloud platform, support vector machine algorithm

Abstract

Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.

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Author Biographies

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

Chaoqin Bai graduated from Southwest Jiaotong University with a bachelor’s degree in architecture in 2004 and a master’s degree in architecture in 2007. He is currently a doctoral candidate in Earth and Human Settlement Science and Engineering at Xi’an Jiaotong University. Currently, he is an associate professor and master tutor of the Department of Architecture, College of Civil Engineering and Architecture, Henan University of Science and Technology. He is a director of the Architectural Education Branch and the Local Architecture Branch of the Architectural Society of China. He is mainly engaged in research and teaching work in architectural design and theory, architectural history and cultural heritage protection, building energy conservation and sustainable development, building information technology and digital design.

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

Junrui Liu graduated from Southwest Jiaotong University with a Master’s degree in Architecture in 2009 and Tongji University with a Doctor’s degree in architecture in 2021. Currently, he is a lecturer of the Department of Architecture, College of Civil Engineering and Architecture, Henan University of Science and Technology. He is mainly engaged in research and teaching work in architectural history and theory, conservation of architectural heritage and sustainable development.

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Published

2024-01-14

How to Cite

Bai, C. ., & Liu, J. . (2024). Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm. Strategic Planning for Energy and the Environment, 43(02), 357–380. https://doi.org/10.13052/spee1048-5236.4328

Issue

Section

Greener Energy and Sustainable Development with AI-based loT