Online learning-enhanced data-driven model predictive control for optimizing HVAC energy consumption, indoor air quality and thermal comfort

This paper presents a novel data-driven Model Predictive Control (MPC) strategy to optimize the energy consumption of Heating, Ventilation and Air Conditioning (HVAC) systems by considering indoor thermal comfort and Indoor Air Quality (IAQ). The proposed MPC strategy used an encoder-decoder Long Short-Term Memory (LSTM) model with an online learning optimizer to predict the future energy consumption, IAQ, temperature and relative humidity (RH). A Multiple Objective Particle Swarm Optimization (MOPSO) method with an adaptive weighting strategy for various objectives was used to search for the best control solutions. The adaptive weighting strategy computes the future deviations of the building performance…