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

Henan Zeng and Mohd Fuad Md Arif

International Journal of Low-Carbon Technologies, 2025, vol. 20, 334-340

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.

Keywords: big data analysis; indoor design; energy consumption forecasting; multi-step ahead time series (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:334-340.

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