Data Mining of Building Electrical Information Based on Radial Basis Function Neural Network

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.