Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach
Misbaudeen Aderemi Adesanya, Hammed Obasekore, Anis Rabiu, Wook-Ho Na, Qazeem Opeyemi Ogunlowo, Timothy Denen Akpenpuun, Min‐Hwi Kim, Hyeon Tae Kim, Bo‐Yeong Kang, Hyun-Woo Lee
Topics & Concepts
TRNSYSSetpointHVACPID controllerComputer scienceEnergy consumptionGreenhousePython (programming language)Thermal comfortController (irrigation)Reinforcement learningSimulationControl engineeringControl theory (sociology)Automotive engineeringTemperature controlAir conditioningEnergy (signal processing)EngineeringControl (management)Artificial intelligenceOperating systemMathematicsAgronomyHorticultureThermodynamicsMechanical engineeringBiologyStatisticsPhysicsElectrical engineeringGreenhouse Technology and Climate ControlBuilding Energy and Comfort OptimizationPhotovoltaic System Optimization Techniques