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Impact of artificial intelligence energy management technologies on commercial multi-energy consumption

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  • Fu, Zhong-Lin
  • Cao, Can
  • Gao, Feng

Abstract

This study introduces a novel quantitative framework to deconstruct the “black box” of how AI impacts on commercial multi-energy consumption. We use empirical data from 50 enterprises in Guangzhou to develop two new metrics: “AI application maturity” and “cross-system synergy intensity.” These metrics are used to analyze interactions between ten AI systems and five energy types (cooling, thermal, electricity, gas, and water). The results reveal significant disparities, with the hotel and catering industry achieving the highest maturity (0.82) while the logistics and warehousing sector shows the lowest (0.64). Among technology systems, HVAC systems exhibit the highest AI application maturity (0.81). Notably, AI's energy-saving impacts are highly industry-specific. For example, hotels achieve primary savings in gas while data centers save most in electricity. Crucially, we identify a “system conflict,” where electric energy management involves the most AI systems (10) yet does not achieve the highest cross-system synergy intensity (2.90). This finding validates the necessity for industry-tailored AI strategies and unified intelligent platforms. Ultimately, this study provides a transferable, data-driven framework that facilitates the critical shift from “single-point optimization” to “global synergy.”

Suggested Citation

  • Fu, Zhong-Lin & Cao, Can & Gao, Feng, 2025. "Impact of artificial intelligence energy management technologies on commercial multi-energy consumption," Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225048893
    DOI: 10.1016/j.energy.2025.139247
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    2. Cheng, Yuheng & Zhou, Xiyuan & Zhao, Huan & Liang, Gaoqi & Wen, Fushuan & Zhao, Junhua, 2026. "Secure and trustworthy energy systems: A four-layer threat model and defense-in-depth framework," Energy, Elsevier, vol. 344(C).

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