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Power demand forecasting for demand-driven energy production with biogas plants

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  • Dittmer, Celina
  • Krümpel, Johannes
  • Lemmer, Andreas

Abstract

For the future energy system it becomes increasingly important that biogas plants produce electricity in a demand-oriented way to compensate electricity production from fluctuating sources like wind power and photovoltaics. Flexibilisation concepts provide a coordinated feeding management, which consider different gas production kinetics of used substrates to adjust the biogas production. To enable the generation of a prospective timetable, suitable forecast models for power demand were evaluated. The resulting 48-h forecasts of power demand of a “real-world laboratory” demonstrated that the four selected models achieve comparably good results with a mean absolute percentage error (MAPE) between 13 and 16%. Further evaluation showed that forecasts over longer periods of up to 14 days are advantageous as they are possible without compromising forecast quality.

Suggested Citation

  • Dittmer, Celina & Krümpel, Johannes & Lemmer, Andreas, 2021. "Power demand forecasting for demand-driven energy production with biogas plants," Renewable Energy, Elsevier, vol. 163(C), pages 1871-1877.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:1871-1877
    DOI: 10.1016/j.renene.2020.10.099
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    3. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
    4. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    5. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    6. Orhan Altuğ Karabiber & George Xydis, 2019. "Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods," Energies, MDPI, vol. 12(5), pages 1-29, March.
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    Cited by:

    1. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
    2. D'Aquino, Camila A. & Santos, Samantha C. & Sauer, Ildo L., 2022. "Biogas as an alternative source of decentralized bioelectricity for large waste producers: An assessment framework at the University of São Paulo," Energy, Elsevier, vol. 239(PD).
    3. Liu, Gang & Wang, Kun & Hao, Xiaochen & Zhang, Zhipeng & Zhao, Yantao & Xu, Qingquan, 2022. "SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system," Energy, Elsevier, vol. 241(C).

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