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A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings

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  • Brucke, Karoline
  • Arens, Stefan
  • Telle, Jan-Simon
  • Steens, Thomas
  • Hanke, Benedikt
  • von Maydell, Karsten
  • Agert, Carsten

Abstract

In this study, a new algorithm is developed to extract device profiles in a fully unsupervised manner from three-phases reactive and active aggregate power measurements. The extracted device profiles are then applied to disaggregate the aggregate power measurements by means of particle swarm optimization. Then, a new approach to very short-term power predictions is presented, which makes use of the disaggregation data. For this purpose, a state change forecast is carried out for each device by an artificial neural network and subsequently converted into a power prediction by reconstructing the power profile with respect to the state changes and device profiles. The forecast horizon is 15 min. In order to demonstrate the developed approaches, three-phase reactive and active aggregate power measurements of a multi-tenant commercial building are employed as a case study. The granularity of the data used is 1 s. In total, 52 device profiles are extracted from the aggregate power data. The disaggregation exhibited a highly accurate reconstruction of the measured power with an energy percentage error of approximately 1 %. The indirect power prediction method developed is then applied to the measured power data and outperforms the two persistence forecasts, as well as an artificial neural network designed for 24h-ahead power predictions working in the power domain.

Suggested Citation

  • Brucke, Karoline & Arens, Stefan & Telle, Jan-Simon & Steens, Thomas & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2021. "A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings," Applied Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921003494
    DOI: 10.1016/j.apenergy.2021.116860
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    References listed on IDEAS

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    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Arcos-Aviles, Diego & Pascual, Julio & Guinjoan, Francesc & Marroyo, Luis & Sanchis, Pablo & Marietta, Martin P., 2017. "Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting," Applied Energy, Elsevier, vol. 205(C), pages 69-84.
    3. Amirhosein Torabi & Sayyed Ali Kiaian Mousavy & Vahideh Dashti & Mohammadhossein Saeedi & Nasser Yousefi, 2019. "A New Prediction Model Based on Cascade NN for Wind Power Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1219-1243, March.
    4. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    5. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    6. Andrés M. Alonso & Francisco J. Nogales & Carlos Ruiz, 2020. "A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series," Energies, MDPI, vol. 13(20), pages 1-19, October.
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    3. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).

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