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Algorithmic acquisition of diagnostic patterns in district heating billing system

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  • Kiluk, Sebastian

Abstract

An application of algorithmic exploration of billing data is examined for fault detection, diagnosis (FDD) based on evaluation of present state and detection of unexpected changes in energy efficiency of buildings. Large data sets from district heating (DH) billing systems are used for construction of feature space, diagnostic rules and classification of the buildings according to their energy efficiency properties. The algorithmic approach automates discovering knowledge about common, thus accepted changes in buildings’ properties, in equipment and in habitants’ behavior reflecting progress in technology and life style. In this article implementation of Data Mining and Knowledge Discovery (DMKD) method in supervision system with exemplary results based on real data is presented. Crucial steps of data processing influencing diagnostic results are described in details.

Suggested Citation

  • Kiluk, Sebastian, 2012. "Algorithmic acquisition of diagnostic patterns in district heating billing system," Applied Energy, Elsevier, vol. 91(1), pages 146-155.
    • Handle: RePEc:eee:appene:v:91:y:2012:i:1:p:146-155
    DOI: 10.1016/j.apenergy.2011.09.023
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    1. He, Jiang & Hoyano, Akira & Asawa, Takashi, 2009. "A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance," Applied Energy, Elsevier, vol. 86(9), pages 1596-1605, September.
    2. Bruhns, Harry & Steadman, Philip & Herring, Horace, 2000. "A database for modeling energy use in the non-domestic building stock of England and Wales," Applied Energy, Elsevier, vol. 66(4), pages 277-297, August.
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    Cited by:

    1. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    2. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    3. Kiluk, S., 2014. "Dynamic classification system in large-scale supervision of energy efficiency in buildings," Applied Energy, Elsevier, vol. 132(C), pages 1-14.
    4. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    5. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
    6. Tarek Rakha & Rawad El Kontar, 2019. "Community energy by design: A simulation-based design workflow using measured data clustering to calibrate Urban Building Energy Models (UBEMs)," Environment and Planning B, , vol. 46(8), pages 1517-1533, October.
    7. Blanco, J.M. & Vazquez, L. & Peña, F. & Diaz, D., 2013. "New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants," Applied Energy, Elsevier, vol. 101(C), pages 589-599.
    8. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    9. Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
    10. Sarran, Lucile & Smith, Kevin M. & Hviid, Christian A. & Rode, Carsten, 2022. "Grey-box modelling and virtual sensors enabling continuous commissioning of hydronic floor heating," Energy, Elsevier, vol. 261(PB).

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