Day-ahead optimal scheduling of smart electric storage heaters: A real quantification of uncertainty factors

Optimized controls are particularly promising for flexible and efficient management of space heating cooling systems in buildings. However, when based on predictive models, their effectiveness is affected by the reliability models used. In this paper we propose a quantification analysis some main uncertainty factors that can be observed an optimal control really implemented building. A day-ahead scheduling was applied to system (composed smart electric heaters with thermal storage) single room office building located Osimo (Italy). The algorithm formulated determine charging periods objective minimizing withdrawal energy from grid. takes into account electricity produced photovoltaic plant must maintain internal air temperature close imposed setpoint. Firstly, actual application shown during two selected days. Secondly, extended quantify impact performance prediction input variables. variable has greatest weather forecast and, specifically, cloudiness index, which determines solar gains. different moment time predicted proved have significant (expected variation ranges -50% + 100%) indoor (variations up 40%).