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A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings

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Abstract

In this paper, a tool for the detection and diagnosis of anomalous electrical daily energy patterns relative to a transformer substation of a university campus was developed and tested. Through an innovative pattern recognition analysis consisting in a multi-step clustering process, six clusters of anomalous daily load profiles were identified and isolated in two-year historical data of total electrical energy consumption. The infrequent electrical load profiles were found to be strongly affected, in terms of both shape and magnitude, by the energy consumption behaviour related to the heating/cooling mechanical room. Then, a fault-free predictive model, which uses artificial neural network (ANN) in combination with a Regression Tree, was developed to detect anomalous trends of the electrical energy consumption. The model was able to detect the 93.7% of the anomalous profiles and only the 5% of fault-free days were wrongly predicted as anomalous. Eventually, a diagnosis phase was conceived and validated with a testing data set. A number of daily abnormal load profiles were detected and compared with the centroids of the anomalous clusters identified in the pattern-recognition stage. The work led to the development of a flexible intelligent tool useful for operating a continuous commissioning of the campus facilities.

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Acknowledgements

The authors express their gratitude to Living Lab of PoliTo for providing data and to Eng. Giovanni Carioni for the support in data preparation and collection. The authors also gratefully acknowledge the support of this research by the Research Grant Council of the Hong Kong SAR (152133/19E).

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Authors and Affiliations

  1. Department of Energy “Galileo Ferraris”, TEBE research group, Politecnico di Torino, Turin, Italy

    Marco Savino Piscitelli, Silvio Brandi & Alfonso Capozzoli

  2. Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Marco Savino Piscitelli & Fu Xiao

Authors
  1. Marco Savino Piscitelli
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  2. Silvio Brandi
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  3. Alfonso Capozzoli
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  4. Fu Xiao
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Corresponding author

Correspondence to Alfonso Capozzoli.

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Piscitelli, M.S., Brandi, S., Capozzoli, A. et al. A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings. Build. Simul. 14, 131–147 (2021). https://doi.org/10.1007/s12273-020-0650-1

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  • DOI: https://doi.org/10.1007/s12273-020-0650-1

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