Weibull distribution model for the characterization of aggregate load patterns

Probabilistic Modeling of electric load is a key aspect for the study of distribution system. Characteristics of electric load patterns are extracted by using appropriate probabilistic model. Characterization of aggregated load pattern is very helpful for the system operator or aggregator at microgrid level. Inter-temporal evaluation of electric load patterns is a challenging task. Intertemporal load patterns behavior of residential consumers are extracted by using Weibull distribution and generalized regression neural network. Weibull distribution based probabilistic model with neural network is used for the generation of load patterns from the characteristics extracted from the reference load patterns. Generated load patterns are useful for the scenario analysis, offline testing of power system, distributed generation studies, analysis of equipment before installation. Goodness of Fit (GOF) indicators are used for calculating the accuracy and validation of proposed probabilistic model.