Decomposition and statistical analysis for regional electricity demand forecasting
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DOI: 10.1016/j.energy.2012.03.011
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- Stephen J. Déry & Marco A. Hernández-Henríquez & Tricia A. Stadnyk & Tara J. Troy, 2021. "Vanishing weekly hydropeaking cycles in American and Canadian rivers," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
- Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
- Kofi Afrifa Agyeman & Gyeonggak Kim & Hoonyeon Jo & Seunghyeon Park & Sekyung Han, 2020. "An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads," Energies, MDPI, vol. 13(10), pages 1-20, May.
- Kornkamol Laung-Iem & Prapita Thanarak, 2021. "Forecasting of Biodiesel Prices in Thailand using Time Series Decomposition Method for Long Term from 2017 to 2036," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 593-600.
- Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
- Mirlatifi, A.M. & Egelioglu, F. & Atikol, U., 2015. "An econometric model for annual peak demand for small utilities," Energy, Elsevier, vol. 89(C), pages 35-44.
- Morini, Mirko & Pinelli, Michele & Spina, Pier Ruggero & Venturini, Mauro, 2013. "Optimal allocation of thermal, electric and cooling loads among generation technologies in household applications," Applied Energy, Elsevier, vol. 112(C), pages 205-214.
- 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.
- Higgins, Andrew & Grozev, George & Ren, Zhengen & Garner, Stephen & Walden, Glenn & Taylor, Michelle, 2014. "Modelling future uptake of distributed energy resources under alternative tariff structures," Energy, Elsevier, vol. 74(C), pages 455-463.
- Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
- Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
- Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
- Dadkhah, Mojtaba & Jahangoshai Rezaee, Mustafa & Zare Chavoshi, Ahmad, 2018. "Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed," Energy, Elsevier, vol. 148(C), pages 775-788.
- Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
- Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
- Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
- Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
- Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.
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Keywords
Average electricity demand; Peak electricity demand; Probabilistic; Trend; Variability; Projection;All these keywords.
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