Application and challenges of big data analytics in low-carbon indoor space design

• Published in: INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES
• Main Authors: Zeng, Henan; Arif, Mohd Fuad Md
• Format: Article
• Language: English
• Published: OXFORD UNIV PRESS 2025
• Subjects: Thermodynamics; Energy & Fuels
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001416683900001

The techniques of big data analysis hold immense potential in optimizing indoor energy consumption and enhancing comfort levels. This paper proposes a predictive method for effectively forecasting energy usage in libraries through a multi-step ahead time series-based long short-term memory-backpropagation model, integrated with building energy consumption sub-metering analysis technology. Experimental results indicate that the proposed multi-input multi-output model significantly outperforms traditional recursive and direct models in terms of predictive performance, adeptly capturing the intricate characteristics and temporal dependencies of energy consumption data, thereby offering a novel technological pathway and practical implications for building energy management.