Development of improved adaptive approaches to electricity demand forecasting
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DOI: 10.1057/palgrave.jors.2602447
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Cited by:
- Taylor, James W. & Snyder, Ralph D., 2012.
"Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing,"
Omega, Elsevier, vol. 40(6), pages 748-757.
- James W. Taylor & Ralph D. Snyder, 2009. "Forecasting Intraday Time Series with Multiple Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 9/09, Monash University, Department of Econometrics and Business Statistics.
- Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
- Lifang Xiao & Xiangyang Chen & Hao Wang, 2021. "Calculation and realization of new method grey residual error correction model," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-13, July.
- 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.
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Keywords
neural networks; electricity markets; forecasting comparisons; state space models; unobserved components models;All these keywords.
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