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Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison

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  • Carpenter, Joseph
  • Woodbury, Keith A.
  • O'Neill, Zheng

Abstract

Industrial facilities account for approximately a third of energy usage in the world, and effective energy assessments of these facilities require a reliable baseline energy model. Commercial and residential buildings have been baselined with both simple change-point models and models that are more complex, such as Gaussian process and artificial neural networks, and these models are developed and tested with dense high-frequency data. However, industrial facilities have only been baselined using change-point models, and data for the models are typically restricted to monthly utility bills and, therefore, generally sparse data.

Suggested Citation

  • Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
    • Handle: RePEc:eee:appene:v:213:y:2018:i:c:p:415-425
    DOI: 10.1016/j.apenergy.2018.01.043
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    References listed on IDEAS

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    1. Trianni, Andrea & Cagno, Enrico & Farné, Stefano, 2016. "Barriers, drivers and decision-making process for industrial energy efficiency: A broad study among manufacturing small and medium-sized enterprises," Applied Energy, Elsevier, vol. 162(C), pages 1537-1551.
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    3. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
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    Cited by:

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    2. Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
    3. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    4. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
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    6. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).

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