Detecting patterns of appliances from total load data using a dynamic programming approach

Nonintrusive appliance load monitoring (NIALM) systems 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 but the loss of information of the measured data has to be compensated by more intelligent algorithms and implemented rules to disaggregate the total load trace of only the active power measurements. The paper presents a NIALM approach to analyse data, collected from a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of clustering methods, a genetic algorithm and a dynamic programming approach is presented. The genetic algorithm is used to combine frequently occurring events to create hypothetical finite state machines to model detectable appliances. The time series of each finite state machine is optimized using a dynamic programming method similar to the viterbi algorithm.