Stochastic Generation of District Heat Load

Modelling heat load is a crucial challenge for the proper management of production and distribution. Several studies have tackled this issue at building urban levels, however, current scale interest shifting to district level due new paradigm smart system. This study presents stochastic procedure model with different number buildings aggregation. The proposed method based on superimposition approach by analysing seasonal component using linear regression outdoor temperature intra-daily through bi-parametric distribution times day. Moreover, an empirical relationship, that estimates demand variation given average together user aggregation coefficient, proposed. To assess effectiveness methodology, group residential users connected heating system Bozen-Bolzano carried out. In addition, application three-day prevision shows suitability approach. final purpose provide flexible tool characterisation sample time series data summary information about belonging analysed district.