HVAC system energy optimization using an adaptive hybrid metaheuristic

Previous research efforts for optimizing energy usage of HVAC systems require either mathematical models of HVAC systems to be built or they require substantial historical operational data for learning the optimal operational settings. We introduce a model-free control policy that begins learning optimal settings with no prior historical data and optimizes HVAC operations. The control policy is an adaptive hybrid metaheuristic that uses real-time data, stored in building automation systems (e.g., gas/electricity consumption, weather, and occupancy), to find optimal setpoints at the building level and controls the setpoints accordingly. The algorithm consists of metaheuristic (k-nearest neighbor stochastic hill climbing), machine learning (regression decision tree), and self-tuning (recursive brute-force search) components. The control policy uses smart selection of daily setpoints as the control basis, making the control schema complementary to legacy building management systems. To evaluate, we used the DOE reference small office building in all U.S. climate zones and simulated different control policies using EnergyPlus. The proposed algorithm resulted in 31.17% energy savings compared to the baseline operations (22.5 °C and 3 K). The algorithm has a superior performance in all climate zones for the goodness of measure (i.e., normalized root mean square error) with a value of 0.047