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

Author

Listed:
  • Henan Zeng
  • Mohd Fuad Md Arif

Abstract

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.

Suggested Citation

  • Henan Zeng & Mohd Fuad Md Arif, 2025. "Application and challenges of big data analytics in low-carbon indoor space design," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 334-340.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:334-340.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf005
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