Data mining of building electrical information, based on radial basis function neural network

Norman C. F. Tse; Wing W. Y. Ng; T. T. Chow; John Chan; L. L. Lai; Daniel S. Yeung; Jincheng Li

doi:10.1109/ISAP.2009.5352854

Data mining of building electrical information, based on radial basis function neural network

Norman C. F. Tse^*, Wing W. Y. Ng, T. T. Chow, John Chan, L. L. Lai, Daniel S. Yeung, Jincheng Li

^*Corresponding author for this work

City University of Hong Kong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper presents a neural network algorithm for data mining in building LV electrical power information. The power information is recorded by web-based power quality monitoring system. Power information is recorded continuously and stored in a central server system. Presently events were identified by power engineers but in the prototype, an expert system will be used to identify events instead. Neural network approach based on the Radial Basis Function Neural Network (RBFNN) was developed to predict power events in the building LV electrical network. The approach provides useful information for facility managers to conduct planning and operation. The proposed algorithm was tested with power data of a commercial building in Hong Kong. The prediction result by using one week of data achieved 75% accuracy. Further works would be conducted to test the algorithm with more data. © 2009 IEEE.

Original language	English
Title of host publication	2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
DOIs	https://doi.org/10.1109/ISAP.2009.5352854
Publication status	Published - 9 Dec 2009
Event	2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09 - Curitiba, Brazil Duration: 8 Nov 2009 → 12 Nov 2009

Conference

Conference	2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
Country/Territory	Brazil
City	Curitiba
Period	8/11/09 → 12/11/09

Research Keywords

Building LV electrical network
Data mining
Micro-grid
Neural network
PQ monitoring

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10.1109/ISAP.2009.5352854

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@inproceedings{2dffe40046e9427881a69b6cccbe2c9e,

title = "Data mining of building electrical information, based on radial basis function neural network",

abstract = "This paper presents a neural network algorithm for data mining in building LV electrical power information. The power information is recorded by web-based power quality monitoring system. Power information is recorded continuously and stored in a central server system. Presently events were identified by power engineers but in the prototype, an expert system will be used to identify events instead. Neural network approach based on the Radial Basis Function Neural Network (RBFNN) was developed to predict power events in the building LV electrical network. The approach provides useful information for facility managers to conduct planning and operation. The proposed algorithm was tested with power data of a commercial building in Hong Kong. The prediction result by using one week of data achieved 75% accuracy. Further works would be conducted to test the algorithm with more data. {\textcopyright} 2009 IEEE.",

keywords = "Building LV electrical network, Data mining, Micro-grid, Neural network, PQ monitoring",

author = "Tse, {Norman C. F.} and Ng, {Wing W. Y.} and Chow, {T. T.} and John Chan and Lai, {L. L.} and Yeung, {Daniel S.} and Jincheng Li",

year = "2009",

month = dec,

day = "9",

doi = "10.1109/ISAP.2009.5352854",

language = "English",

isbn = "9781424450985",

booktitle = "2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09",

note = "2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09 ; Conference date: 08-11-2009 Through 12-11-2009",

}

Tse, NCF, Ng, WWY, Chow, TT, Chan, J, Lai, LL, Yeung, DS & Li, J 2009, Data mining of building electrical information, based on radial basis function neural network. in 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09., 5352854, 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09, Curitiba, Brazil, 8/11/09. https://doi.org/10.1109/ISAP.2009.5352854

Data mining of building electrical information, based on radial basis function neural network. / Tse, Norman C. F.; Ng, Wing W. Y.; Chow, T. T. et al.
2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09. 2009. 5352854.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Data mining of building electrical information, based on radial basis function neural network

AU - Tse, Norman C. F.

AU - Ng, Wing W. Y.

AU - Chow, T. T.

AU - Chan, John

AU - Lai, L. L.

AU - Yeung, Daniel S.

AU - Li, Jincheng

PY - 2009/12/9

Y1 - 2009/12/9

N2 - This paper presents a neural network algorithm for data mining in building LV electrical power information. The power information is recorded by web-based power quality monitoring system. Power information is recorded continuously and stored in a central server system. Presently events were identified by power engineers but in the prototype, an expert system will be used to identify events instead. Neural network approach based on the Radial Basis Function Neural Network (RBFNN) was developed to predict power events in the building LV electrical network. The approach provides useful information for facility managers to conduct planning and operation. The proposed algorithm was tested with power data of a commercial building in Hong Kong. The prediction result by using one week of data achieved 75% accuracy. Further works would be conducted to test the algorithm with more data. © 2009 IEEE.

AB - This paper presents a neural network algorithm for data mining in building LV electrical power information. The power information is recorded by web-based power quality monitoring system. Power information is recorded continuously and stored in a central server system. Presently events were identified by power engineers but in the prototype, an expert system will be used to identify events instead. Neural network approach based on the Radial Basis Function Neural Network (RBFNN) was developed to predict power events in the building LV electrical network. The approach provides useful information for facility managers to conduct planning and operation. The proposed algorithm was tested with power data of a commercial building in Hong Kong. The prediction result by using one week of data achieved 75% accuracy. Further works would be conducted to test the algorithm with more data. © 2009 IEEE.

KW - Building LV electrical network

KW - Data mining

KW - Micro-grid

KW - Neural network

KW - PQ monitoring

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U2 - 10.1109/ISAP.2009.5352854

DO - 10.1109/ISAP.2009.5352854

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424450985

BT - 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

T2 - 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

Y2 - 8 November 2009 through 12 November 2009

ER -

Data mining of building electrical information, based on radial basis function neural network

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