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

• Published in: International Journal of Low-Carbon Technologies
• Main Author: Zeng H.; Arif M.F.M.
• Format: Article
• Language: English
• Published: Oxford University Press 2025
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217788406&doi=10.1093%2fijlct%2fctaf005&partnerID=40&md5=e6d5718c937a1025d5aa313203be7e1d

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. © 2025 The Author(s). Published by Oxford University Press.