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Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption

Author

Listed:
  • Yu Cui

    (Center for Sustainable Energy Technologies, Energy and Environment Institute, University of Hull, Hull HU6 7RX, UK)

  • Zishang Zhu

    (Center for Sustainable Energy Technologies, Energy and Environment Institute, University of Hull, Hull HU6 7RX, UK)

  • Xudong Zhao

    (Center for Sustainable Energy Technologies, Energy and Environment Institute, University of Hull, Hull HU6 7RX, UK)

  • Zhaomeng Li

    (Center for Sustainable Energy Technologies, Energy and Environment Institute, University of Hull, Hull HU6 7RX, UK)

Abstract

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance.

Suggested Citation

  • Yu Cui & Zishang Zhu & Xudong Zhao & Zhaomeng Li, 2023. "Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8750-:d:1158510
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    References listed on IDEAS

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    1. Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019. "Energy saving estimation for plug and lighting load using occupancy analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1143-1161.
    2. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    3. Panchabikesan, Karthik & Haghighat, Fariborz & Mankibi, Mohamed El, 2021. "Data driven occupancy information for energy simulation and energy use assessment in residential buildings," Energy, Elsevier, vol. 218(C).
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