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Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism

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  • Khan, Waqas
  • Liao, Juo Yu
  • Walker, Shalika
  • Zeiler, Wim

Abstract

This study focuses on studying the impact of temporal resolution of energy data from a commercial building on data exploration and learning applications. The goal is to improve the consistency of the modelling techniques and define the best-unified resolution of data for different applications such as data exploration, consistency, load profile extraction, and forecasting for commercial buildings. A three-step process is proposed to evaluate the data resolution effects on mining and learning applications. The first step, the data exploration and consistency, transforms the raw data into a human interpretable form using different temporal features to understand the dependency of the load consumption of the building. In the second step, the K-means clustering technique is used to extract the typical load profiles for all data granularities to deduce information related to the operational behaviour of the building throughout the year. Finally, the long short-term memory model is evaluated for building load forecasting in a univariate and multivariate approach in the last step. The results demonstrate that higher-resolution data does not necessarily assure clear relationships between the operational parameters of a commercial building using data exploration techniques. Furthermore, increasing the granularity of the data did not affect the extracted number of clusters or the load profiles overall shape (peak points). In contrast, to load profile extraction, the obtained results are improved in building load forecasting with NRMSE from 0.125 to 0.028 for daily to 1 min resolution data. Overall, considering the balance of accuracy and processing time the 15-minute resolution data with a univariate approach can perform the best.

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  • Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:appene:v:319:y:2022:i:c:s0306261922006389
    DOI: 10.1016/j.apenergy.2022.119281
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    References listed on IDEAS

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    1. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
    2. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    3. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    4. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    6. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
    7. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    8. Fridgen, Gilbert & Michaelis, Anne & Rinck, Maximilian & Schöpf, Michael & Weibelzahl, Martin, 2020. "The search for the perfect match: Aligning power-trading products to the energy transition," Energy Policy, Elsevier, vol. 144(C).
    9. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    10. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
    11. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    12. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    13. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    14. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    15. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    16. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
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    2. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).

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