摘要：As a means to adjust the temperature of the thermal zones in buildings, building thermal mass is regarded as one of the essential sources of energy flexibility. It is still challenging to quantify the energy flexibility of passive thermal mass, making it oppugning to use thermal mass for buildings’ demand response (DR). A method to accurately quantify the energy flexibility from heating, ventilation, and air conditioning systems (HVAC) is important for buildings to participate in DR projects. This paper proposes a novel data-driven model to quantify HVAC’s electrical demand under dynamic global temperature adjustment. The Markov chain is first used to implement an effective sampling method to produce a global temperature resetting schedule representing different temperature resetting. Next, EnergyPlus evaluates the HVAC electrical demand under the various temperature reset scenarios. In the end, the LightGBM algorithm is used to develop the data-driven model. Having validated the proposed model, the case study was conducted in a DOE reference office building for EnergyPlus. Results demonstrate that the Markov chain outperforms the probabilistic method when sampling temperature setpoint schedules. In the future, the proposed data-driven model can be used to evaluate the flexibility capacity of an energy management system in grid-integrated buildings.
