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Load forecasting of supermarket refrigeration

Author

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  • Rasmussen, Lisa Buth
  • Bacher, Peder
  • Madsen, Henrik
  • Nielsen, Henrik Aalborg
  • Heerup, Christian
  • Green, Torben

Abstract

This paper presents a novel study of models for forecasting the electrical load for supermarket refrigeration. The data used for building the models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village in Denmark. Every hour the hourly electrical load for refrigeration is forecasted for the following 42h. The forecast models are adaptive linear time series models. The model has two regimes; one for opening hours and one for closing hours, this is modeled by a regime switching model and two different methods for predicting the regimes are tested. The dynamic relation between the weather and the load is modeled by simple transfer functions and the non-linearities are described using spline functions. The results are thoroughly evaluated and it is shown that the spline functions are suitable for handling the non-linear relations and that after applying an auto-regressive noise model the one-step ahead residuals do not contain further significant information.

Suggested Citation

  • Rasmussen, Lisa Buth & Bacher, Peder & Madsen, Henrik & Nielsen, Henrik Aalborg & Heerup, Christian & Green, Torben, 2016. "Load forecasting of supermarket refrigeration," Applied Energy, Elsevier, vol. 163(C), pages 32-40.
    • Handle: RePEc:eee:appene:v:163:y:2016:i:c:p:32-40
    DOI: 10.1016/j.apenergy.2015.10.046
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    References listed on IDEAS

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    1. Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
    2. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    3. 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.
    4. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
    5. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    6. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    7. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, vol. 5(3), pages 1-37, March.
    8. Marcelo Espinoza & Johan Suykens & Bart Moor, 2006. "Fixed-size Least Squares Support Vector Machines: A Large Scale Application in Electrical Load Forecasting," Computational Management Science, Springer, vol. 3(2), pages 113-129, April.
    9. S. Ehsan Shafiei & Henrik Rasmussen & Jakob Stoustrup, 2013. "Modeling Supermarket Refrigeration Systems for Demand-Side Management," Energies, MDPI, vol. 6(2), pages 1-21, February.
    10. Hovgaard, Tobias Gybel & Larsen, Lars F.S. & Edlund, Kristian & Jørgensen, John Bagterp, 2012. "Model predictive control technologies for efficient and flexible power consumption in refrigeration systems," Energy, Elsevier, vol. 44(1), pages 105-116.
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    Cited by:

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    3. Postnikov, A. & Albayati, I.M. & Pearson, S. & Bingham, C. & Bickerton, R. & Zolotas, A., 2019. "Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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    5. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    6. Bergsteinsson, Hjörleifur G. & Møller, Jan Kloppenborg & Nystrup, Peter & Pálsson, Ólafur Pétur & Guericke, Daniela & Madsen, Henrik, 2021. "Heat load forecasting using adaptive temporal hierarchies," Applied Energy, Elsevier, vol. 292(C).
    7. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.

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