To realize friendly interaction between building heating

ventilation and air conditioning（HVAC）and the power grid

a demand response cost assessment method combining model and data driven is proposed. Firstly

the fitting ability of artificial neural network（ANN） is used to automatically learn from the physical model and build a dynamic model of HVAC. Then

considering the impact of temperature changes on the personnel efficiency of building

an optimization model of the HVAC setting temperature is established and solved based on particle swarm optimization（PSO）algorithm. Finally

additional cost incurred by users due to deviation from the optimal set temperature is defined as response cost

and demand response cost assessment for HVAC is proposed accordingly. Simulation results show that by setting optimal temperature of HVAC

total cost of users can be significantly reduced. The response cost curve can reflect users’productivity loss factors

thereby helping users determine the bid price rationally in demand response.