Abstract
This paper presents a model predictive control (MPC) methodology for integrating air-based photovoltaic/thermal (PV/T) systems in school buildings. The methodology is developed based on a case study for an archetype fully electric school building in Québec, Canada. A data‐driven grey‐box model for the classrooms is calibrated with measured data, and a PV/T model is developed. These models are integrated to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. Three scenarios are investigated and compared: (1) A reference case without a PV or PV/T system, (2) Integration of a PV system and MPC, and (3) Integration of a PV/T system and MPC under a demand response scenario. Results show that using the MPC with PV/T integration can reduce peak demand by up to 100% during high-demand periods for the grid. This methodology is scalable and can be transferable to other institutional buildings. Abbreviations: PV: Photovoltaics system; BIPV/T: Building integrated photovoltaic and thermal system; COP: Coefficient of performance; CV-RMSE: Root-mean-square error; DR: Demand response; DSM: Demand-side management; HRV: Heat recovery ventilator; HVAC: Heating ventilation and air conditioning; IEQ: Indoor environmental quality; MPC: Model predictive control; RC model: Resistance-capacitance model; RES: Renewable energy sources
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