Analysis and Predicting the Energy Consumption of Low-Pressure Carburising Processes
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- Cauchi, Nathalie & Macek, Karel & Abate, Alessandro, 2017. "Model-based predictive maintenance in building automation systems with user discomfort," Energy, Elsevier, vol. 138(C), pages 306-315.
- Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
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Keywords
energy consumption of low-pressure carburising processes; analysis of the energy intensity; statistical analysis;All these keywords.
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