Economic Load Demand Response in Energy Management Information Systems: Data Mining Methods in the Safety and Quality of Building Systems

Coordinating economic load demand response (ELDR) strategy with energy efficiency and information technology of e-business multiplies the reduction in electricity usage. The real power, reactive power, and total harmonic distortions of voltage and current of steady-state power signatures contain plenty of information needed for detecting state transition and aging of loads. On the other hand, adopting the values of power quality directly has the drawbacks of taking a longer time and much memory for the data mining system of energy management information system (EMIS). To effectively reduce the number of power signatures representing load state transition and aging signals, a cluster classification technique of the power signatures in the data mining system with different mean and standard deviations calculated by the He linger distance are presented in this paper. The high success rates of detecting state transition and aging of loads from the proposed method have been proved via experiments to be feasible in load operations of EMIS applications.