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Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms

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  • Zhang, Chengyu
  • Ma, Liangdong
  • Han, Xing
  • Zhao, Tianyi

Abstract

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system.

Suggested Citation

  • Zhang, Chengyu & Ma, Liangdong & Han, Xing & Zhao, Tianyi, 2024. "Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031936
    DOI: 10.1016/j.energy.2023.129799
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Chengyu & Luo, Zhiwen & Rezgui, Yacine & Zhao, Tianyi, 2024. "Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a univer," Energy, Elsevier, vol. 294(C).
    2. Zhang, Chengyu & Rezgui, Yacine & Luo, Zhiwen & Jiang, Ben & Zhao, Tianyi, 2024. "Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation," Applied Energy, Elsevier, vol. 373(C).
    3. Zhitao Wang & Yubin Qiu & Shiyu Zhou & Yanfa Tian & Xiangyuan Zhu & Jiying Liu & Shengze Lu, 2025. "Enhancing Air Conditioning System Efficiency Through Load Prediction and Deep Reinforcement Learning: A Case Study of Ground Source Heat Pumps," Energies, MDPI, vol. 18(1), pages 1-26, January.

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