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A new error measure for forecasts of household-level, high resolution electrical energy consumption

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  • Haben, Stephen
  • Ward, Jonathan
  • Vukadinovic Greetham, Danica
  • Singleton, Colin
  • Grindrod, Peter

Abstract

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the individual household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the most appropriate charging and discharging cycles. However, before such methods can be developed, validation measures which can assess the accuracy and usefulness of forecasts of the volatile and noisy household-level demand are required. In this paper we introduce a new forecast verification error measure that reduces the so-called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as the Mean Absolute Error, and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point-wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters, and discuss the effect of the permutation restriction.

Suggested Citation

  • Haben, Stephen & Ward, Jonathan & Vukadinovic Greetham, Danica & Singleton, Colin & Grindrod, Peter, 2014. "A new error measure for forecasts of household-level, high resolution electrical energy consumption," International Journal of Forecasting, Elsevier, vol. 30(2), pages 246-256.
    • Handle: RePEc:eee:intfor:v:30:y:2014:i:2:p:246-256
    DOI: 10.1016/j.ijforecast.2013.08.002
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    References listed on IDEAS

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    1. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    2. Taylor, James W. & Espasa, Antoni, 2008. "Energy forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 561-565.
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    Cited by:

    1. Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
    2. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2015. "Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks," Applied Energy, Elsevier, vol. 146(C), pages 122-134.
    3. Colin Singleton & Peter Grindrod, 2021. "Forecasting for Battery Storage: Choosing the Error Metric," Energies, MDPI, vol. 14(19), pages 1-11, October.
    4. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    5. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    6. Andrés Camero & Gabriel Luque & Yesnier Bravo & Enrique Alba, 2018. "Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case," Energies, MDPI, vol. 11(7), pages 1-14, July.
    7. Jaržemskis Andrius & Jaržemskienė Ilona, 2022. "European Green Deal Implications on Country Level Energy Consumption," Folia Oeconomica Stetinensia, Sciendo, vol. 22(2), pages 97-122, December.
    8. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    9. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    10. Pekka Koponen & Jussi Ikäheimo & Juha Koskela & Christina Brester & Harri Niska, 2020. "Assessing and Comparing Short Term Load Forecasting Performance," Energies, MDPI, vol. 13(8), pages 1-17, April.
    11. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    12. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    13. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    14. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    15. Stefano Pietrosanti & William Holderbaum & Victor M. Becerra, 2016. "Optimal Power Management Strategy for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 9(3), pages 1-17, March.
    16. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    17. Jens Kley-Holsteg & Florian Ziel, 2020. "Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso," Papers 2005.04522, arXiv.org.
    18. Matthew Rowe & Timur Yunusov & Stephen Haben & William Holderbaum & Ben Potter, 2014. "The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction," Energies, MDPI, vol. 7(6), pages 1-24, May.
    19. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    20. Florian Ziel & Kevin Berk, 2019. "Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules," Papers 1910.07325, arXiv.org.
    21. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    22. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

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