Deep Learning for Forecasting Electricity Demand in Taiwan
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- Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
- 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.
- Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
- Robert G. Brown & Richard F. Meyer, 1961. "The Fundamental Theorem of Exponential Smoothing," Operations Research, INFORMS, vol. 9(5), pages 673-685, October.
- de Jong, Pieter & Dargaville, Roger & Silver, Jeremy & Utembe, Steven & Kiperstok, Asher & Torres, Ednildo Andrade, 2017. "Forecasting high proportions of wind energy supplying the Brazilian Northeast electricity grid," Applied Energy, Elsevier, vol. 195(C), pages 538-555.
- Sovacool, Benjamin K., 2009. "The importance of comprehensiveness in renewable electricity and energy-efficiency policy," Energy Policy, Elsevier, vol. 37(4), pages 1529-1541, April.
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- Sandipa Bhattacharya & Mitali Sarkar & Biswajit Sarkar & Lakshmi Thangavelu, 2023. "Exploring Sustainability and Economic Growth through Generation of Renewable Energy with Respect to the Dynamical Environment," Mathematics, MDPI, vol. 11(19), pages 1-22, September.
- Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
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Keywords
alternative energy; power generation forecasting; gated recurrent units;All these keywords.
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