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Functional time series approach for forecasting very short-term electricity demand

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  • Han Lin Shang

Abstract

This empirical paper presents a number of functional modelling and forecasting methods for predicting very short-term (such as minute-by-minute) electricity demand. The proposed functional methods slice a seasonal univariate time series (TS) into a TS of curves; reduce the dimensionality of curves by applying functional principal component analysis before using a univariate TS forecasting method and regression techniques. As data points in the daily electricity demand are sequentially observed, a forecast updating method can greatly improve the accuracy of point forecasts. Moreover, we present a non-parametric bootstrap approach to construct and update prediction intervals, and compare the point and interval forecast accuracy with some naive benchmark methods. The proposed methods are illustrated by the half-hourly electricity demand from Monday to Sunday in South Australia.

Suggested Citation

  • Han Lin Shang, 2013. "Functional time series approach for forecasting very short-term electricity demand," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(1), pages 152-168, January.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:152-168
    DOI: 10.1080/02664763.2012.740619
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    Cited by:

    1. Atefeh Zamani & Hossein Haghbin & Maryam Hashemi & Rob J. Hyndman, 2022. "Seasonal functional autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 197-218, March.
    2. repec:hum:wpaper:sfb649dp2016-035 is not listed on IDEAS
    3. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    4. Canale, Antonio & Vantini, Simone, 2016. "Constrained functional time series: Applications to the Italian gas market," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1340-1351.
    5. Elías, Antonio & Jiménez, Raúl & Shang, Han Lin, 2022. "On projection methods for functional time series forecasting," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    6. López Cabrera, Brenda & Schulz, Franziska, 2016. "Time-adaptive probabilistic forecasts of electricity spot prices with application to risk management," SFB 649 Discussion Papers 2016-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Brenda López Cabrera & Franziska Schulz, 2017. "Forecasting Generalized Quantiles of Electricity Demand: A Functional Data Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 127-136, January.
    8. Moustris, K. & Kavadias, K.A. & Zafirakis, D. & Kaldellis, J.K., 2020. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data," Renewable Energy, Elsevier, vol. 147(P1), pages 100-109.
    9. Germán Aneiros & Paula Raña & Philippe Vieu & Juan Vilar, 2018. "Bootstrap in semi-functional partial linear regression under dependence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 659-679, September.
    10. Antoniadis, Anestis & Brossat, Xavier & Cugliari, Jairo & Poggi, Jean-Michel, 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 939-947.
    11. repec:hum:wpaper:sfb649dp2014-030 is not listed on IDEAS
    12. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    13. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    14. Jorge Barrientos Marin & Laura Marquez Marulanda & Fernando Villada Duque, 2023. "Analyzing Electricity Demand in Colombia: A Functional Time Series Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 75-84, January.
    15. Santiago Gall n & Jorge Barrientos, 2021. "Forecasting the Colombian Electricity Spot Price under a Functional Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 67-74.

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