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

Ning Ling Wang; De Gang Chen; Yong Ping Yang

doi:10.4028/www.scientific.net/AMR.860-863.1862

Paper Titles

Research on Application of Synchronized Phasor Measurement Unit (PMU) for Smart Substation
p.1842

Fault Response of Inverter-Based Distributed Generators in Distribution Network
p.1846

Demand Response Based Household Electricity Intelligent Interactive Terminal Design
p.1853

Online Decision Support Index System Suite for Limiting Short Circuit Current
p.1857

Big Data Analytics-Based Energy-Consumption Feature Selection of Large Thermal Power Units
p.1862

Optimized Control of DC Voltage Based on BP Neural Network in Active Power Filter
p.1867

Smart Power Meter System Based on ZigBee
p.1877

The Comprehensive Efficiency Evaluation of Electrical Power System of China Based on Data Envelopment Analysis
p.1881

Study on Reliability Evaluation Method of Components without Enough Fault Data
p.1886

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863Big Data Analytics-Based Energy-Consumption...

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.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

1862-1866

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.1862

Citation:

Cite this paper

Online since:

December 2013

Authors:

Ning Ling Wang*, De Gang Chen, Yong Ping Yang

Keywords:

Big Data Analytics, Feature Selection, Key Energy-Consumption Feature, Large Thermal Power Units

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Valero, A., et al., On the thermoeconomic approach to the diagnosis of energy system malfunctions. Energy, 2004. 29(12~15): 1875~1887.

DOI: 10.1016/j.energy.2004.04.053

Google Scholar

[2] Wang Ningling, Yang Yongping, Yang Zhiping et al. Diagnosis of energy-saving potential and optimized measures for 600MW supercritical coal-fired power units. Proceedings of ICECE, June, (2010).

DOI: 10.1109/icece.2010.890

Google Scholar

[3] Honghuo Hu, Determination of optimal targets of thermal power units．China Electric Power, 2004, 37(9): 22~25.

Google Scholar

[4] A. Kusiak, Z. Song. Clustering-based performance optimization of the boiler-turbine system. IEEE Transactions on Energy Conversion, 2008, 23(2): 651~657.

DOI: 10.1109/tec.2007.914183

Google Scholar

[5] Jiang-qiang Li, Cheng-lin Niu, Ji-zhen Liu. Research and application of data mining in power plant process control and optimization. Advances in Machine Learning and Cybernetics, 2006, 149~158.

DOI: 10.1109/icmlc.2005.1527208

Google Scholar

[6] D. G. Chen, X. Z. Wang, S. Y. Zhao. Attributes reduction based on fuzzy rough sets. RSEISP 2007, LNAI 4585(2007) 381~390.

Google Scholar

[7] D. G. Chen, S. Y. Zhao. Local reduction of decision system with fuzzy rough sets. Fuzzy Sets and Systems, 2010, 161(13): 1871~1883.

DOI: 10.1016/j.fss.2009.12.010

Google Scholar

[8] Eric Tsang, Zhao Suyun. Decision table reduction in KDD: fuzzy rough based approach, Transactions on Rough Sets XI, LNCS 5946, p.177~188, (2010).

DOI: 10.1007/978-3-642-11479-3_10

Google Scholar