Ricerc@Sapienza

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.

A numerical approach for generating a limited number of water demand scenarios and estimating their occurrence probabilities in a water distribution network (WDN) is proposed. This approach makes use of the demand scaling laws in order to consider the natural variability and spatial correlation of nodal consumption. The scaling laws are employed to determine the statistics of nodal consumption as a function of the number of users and the main statistical features of the unitary user's demand. Besides, consumption at each node is considered to follow a Gamma probability distribution.