Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average
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DOI: 10.1016/j.energy.2023.127365
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Cited by:
- Zuin, Gianlucca & Buechler, Rob & Sun, Tao & Zanocco, Chad & Galuppo, Francisco & Veloso, Adriano & Rajagopal, Ram, 2023. "Extreme event counterfactual analysis of electricity consumption in Brazil: Historical impacts and future outlook under climate change," Energy, Elsevier, vol. 281(C).
- Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
- Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
- Carlos Benavides & Sebastián Gwinner & Andrés Ulloa & José Barrales-Ruiz & Vicente Sepúlveda & Manuel Díaz, 2024. "Bus Basis Model Applied to the Chilean Power System: A Detailed Look at Chilean Electric Demand," Energies, MDPI, vol. 17(14), pages 1-28, July.
- Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).
- Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
- Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.
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Keywords
Artificial neural networks; ARIMA; Fourier transform; Wavelet transform; Hybrid models;All these keywords.
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