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A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption

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  • Yu, Xinran
  • Ergan, Semiha
  • Dedemen, Gokmen

Abstract

The electricity consumption of Heating Ventilating and Air Conditioning (HVAC) systems has a significant share in the energy consumption of buildings, which account for 75% of total electricity produced in the US. Therefore, improving the energy efficiency in HVAC systems is an essential goal in facility management (FM) industry. Building Automation Systems (BASs) deployed in buildings provide an enormous amount of data on HVAC operations, which can be leveraged to extract hidden knowledge and insights about operational signatures of these systems (i.e., parameter-value pairs set for running the equipment) and their relationship to energy profiles. This study aims to identify critical parameters of HVAC systems that drive the changes in the building energy-use profiles and develop an automated approach for identifying HVAC operational signatures and their energy profiles in buildings. The approach relies on data-driven methodologies and is composed of three major steps: data preprocessing, feature selection, and signature discovery and analysis. The approach was tested on four air handling units (AHUs) in different buildings. The results showed that it is possible to define operational signatures for facility operators to run AHUs at these custom settings and achieve about 30% saving in electric power, given the profiles across the operational signatures.

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  • Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:3
    DOI: 10.1016/j.apenergy.2019.113497
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    Cited by:

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    5. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    6. Dey, Maitreyee & Rana, Soumya Prakash & Dudley, Sandra, 2021. "Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation – A case study based approach," Applied Energy, Elsevier, vol. 298(C).
    7. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
    8. Hyang-A Park & Gilsung Byeon & Wanbin Son & Jongyul Kim & Sungshin Kim, 2023. "Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin," Energies, MDPI, vol. 16(20), pages 1-14, October.
    9. Yu, Xinran & Ergan, Semiha, 2022. "Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models," Applied Energy, Elsevier, vol. 310(C).
    10. Zhang, Shuyang & Zhang, Lun & Zhang, Xiaosong, 2022. "Clustering based on dynamic time warping to extract typical daily patterns from long-term operation data of a ground source heat pump system," Energy, Elsevier, vol. 249(C).
    11. Li, Bingxu & Cheng, Fanyong & Zhang, Xin & Cui, Can & Cai, Wenjian, 2021. "A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data," Applied Energy, Elsevier, vol. 285(C).

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