A two-stage optimization framework for greenhouse climate control and MPC-based multi-source irrigation scheduling under demand uncertainty

Greenhouse cultivation supports stable food production but faces rising electricity costs, water scarcity, and carbon-emission pressures. This study develops a two-stage optimization framework for sustainable greenhouse operation. The framework couples minute-level climate control with hourly multi-source irrigation scheduling through an evapotranspiration-based water-demand mapping. In the first stage, the climate control regulates temperature, relative humidity, CO2 concentration, and light intensity using a total cost minimization (TCM) strategy that considers time-of-use tariffs and CO2 supply cost, compared with an energy consumption minimization (ECM) strategy. In the second stage, the irrigation scheduling allocates harvested rainwater, groundwater, and municipal water via an irrigation cost minimization (ICM) strategy compared with a rule-based approach. To address water demand uncertainty, a model predictive control (MPC) strategy is proposed to enable real-time dynamic adjustment of irrigation decisions. Results show that, compared with the ECM strategy, the proposed TCM strategy reduces the total operating cost by 36.40%. Compared with the rule-based method, ICM reduces the irrigation cost by 16.19%. MPC maintains supply–demand balance across different demand-uncertainty levels and during sudden demand surges, while achieving lower irrigation costs than the rule-based strategy. This study provides a practical pathway to cost-effective and reliable greenhouse operation under coupled electricity and water constraints.