Genetic algorithm for pattern detection in NIALM systems

Nonintrusive appliance load monitoring systems (NIALM) require sufficient accurate total load data to separate the load into its major appliances. The most available solutions separate the whole electric energy consumption based on the measurement of all three voltages and currents. Aside from the cost for special measuring devices, the intrusion into the local installation is the main problem for reaching a high market distribution. The use of standard digital electricity meters could avoid this problem with loss of information in the measured data. This paper presents a new NIALM approach to analyse data, collected form a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of fuzzy clustering methods, a genetic algorithm and a dynamic programming approach is presented.