Power demand forecasting for demand-driven energy production with biogas plants
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DOI: 10.1016/j.renene.2020.10.099
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- 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).
- Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
- 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.
- 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.
- Adam Clements & Stan Hurn & Zili Li, 2014. "Forecasting day-ahead electricity load using a multiple equation time series approach," NCER Working Paper Series 103, National Centre for Econometric Research, revised 06 May 2015.
- 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.
- 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.
- 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:
- Jindong Yang & Xiran Zhang & Wenhao Chen & Fei Rong, 2024. "Prophet–CEEMDAN–ARBiLSTM-Based Model for Short-Term Load Forecasting," Future Internet, MDPI, vol. 16(6), pages 1-16, May.
- 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.
- 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).
- 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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Keywords
Time series analysis; Prediction; Feeding management; Demand-orientated biogas; ARIMA; TBATS;All these keywords.
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