Model predictive HVAC load control in buildings using real-time electricity pricing

We propose a practical cost and energy efficient model predictive control (MPC) strategy for HVAC load control under dynamic real-time electricity pricing. The MPC strategy is built based on a proposed model that jointly minimizes the total energy consumption and hence, cost of electricity for the user, and the deviation of the inside temperature from the consumer's preference. We develop an algorithm that assigns temperature set-points (reference temperatures) to price ranges based on the consumer's discomfort tolerance index. We also design a practical parameter prediction model for the mapping between the HVAC load and the inside temperature. The prediction model and the produced temperature set-points are integrated as inputs into the MPC controller, which is then used to generate signal actions for the AC unit. To investigate and demonstrate the effectiveness of our approach, we present a simulation based experimental analysis using real-life pricing data. The experiments reveal that the MPC strategy can lead to significant reductions in overall energy consumption and cost savings for the consumer. Results suggest that by providing an efficient response strategy for the consumers, the proposed MPC strategy can enable the utility providers adopt efficient demand management policies using real-time pricing.

► A practical smart control strategy is proposed for HVAC systems in buildings.
► Automated consumer participation through thermal comfort preference is enabled.
► Significant reduction in energy consumption and cost for HVAC systems is achieved.
► This strategy can be helpful in facilitating an effective demand response framework.