Title :
Data Mining of Building Electrical Information Based on Radial Basis Function Neural Network
Author :
Tse, N.C.F. ; Ng, W.W.Y. ; Chow, T.T. ; Chan, J. ; Lai, L.L. ; Yeung, D.S. ; Jincheng Li
Author_Institution :
City Univ. of Hong Kong, Hong Kong, China
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.
Keywords :
data mining; power engineering computing; power system measurement; radial basis function networks; building electrical information; central server system; data mining; power quality monitoring; radial basis function neural network; Data mining; Design engineering; Monitoring; Network servers; Neural networks; Power engineering and energy; Power quality; Prototypes; Radial basis function networks; Testing; PQ monitoring; building LV electrical network; data mining; micro-grid; neural network;
Conference_Titel :
Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on
Conference_Location :
Curitiba
Print_ISBN :
978-1-4244-5097-8
DOI :
10.1109/ISAP.2009.5352854