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Residential consumers can use the demand response program (DRP) if they canutilize the home energy management system (HEMS), which reduces consumer costsby automatically adjusting air conditioning (AC) setpoints and shifting someappliances to off-peak hours. If HEMS knows occupancy status, consumers cangain more economic benefits and thermal comfort. However, for the buildingoccupancy status, direct sensing is costly, inaccurate, and intrusive forresidents. So, forecasting algorithms could serve as an effective alternative.The goal of this study is to present a non-intrusive, accurate, andcost-effective approach, to develop a multi-objective simulation model for theapplication of DRPs in a smart residential house, where (a) electrical loaddemand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints,and (c) , worst cases scenario approach is very conservative. Because that isunlikely all uncertain parameters take their worst values at all times. So, theflexible robust counterpart optimization along with uncertainty budgets isdeveloped to consider uncertainty realistically. Simulated results indicatethat considering uncertainty increases the costs by 36 percent and decreasesthe AC temperature setpoints. Besides, using DRPs reduces demand by shiftingsome appliance operations to off-peak hours and lowers costs by 13.2 percent.

Optimization of Residential Demand Response Program Cost with Consideration for Occupants Thermal Comfort and Privacy