Big Data Analytics-Based Energy-Consumption Feature Selection of Large Thermal Power Units

Abstract

Large coal-fired power unit is a complex nonlinear system with more uncertainty to address, evaluate and optimize. It is essential and difficult to determine the key features contributing to the energy consumption of power units, especially considering the varying boundary constraints, operation conditions and system characteristics. In this paper idea of big data analytics is employed to clean the historian operation data efficiently and select the key energy-consumption features with less information losses. The result shows that the resultant key features reflect the exterior factors and system behavior. It makes great reference for the modeling and optimization of large thermal power units.

Value creation with big data analytics for enterprises: a survey

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