Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States
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DOI: 10.1016/j.energy.2014.08.072
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- Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
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- Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
- Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
- Jihoon Jang & Joosang Lee & Eunjo Son & Kyungyong Park & Gahee Kim & Jee Hang Lee & Seung-Bok Leigh, 2019. "Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection," Energies, MDPI, vol. 12(21), pages 1-20, November.
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Keywords
Energy demand forecast; Industrial sector; Artificial neural networks; Linear regression; Energy modeling; Energy price;All these keywords.
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