Li et al., 2020 - Google Patents
A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy useLi et al., 2020
View PDF- Document ID
- 8093775200290529474
- Author
- Li W
- Wang S
- Publication year
- Publication venue
- Applied Energy
External Links
Snippet
A trade-off problem exists in ventilation systems to ensure acceptable indoor air quality (IAQ) with minimized energy use. It is often solved by the centralized optimization approach today. However, the dynamic operation conditions of ventilation systems and the changing …
- 238000009423 ventilation 0 title abstract description 105
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use | |
Taheri et al. | Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review | |
Li et al. | A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method | |
Satrio et al. | Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm | |
Platt et al. | Adaptive HVAC zone modeling for sustainable buildings | |
Kusiak et al. | Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm | |
Nagarathinam et al. | Marco-multi-agent reinforcement learning based control of building hvac systems | |
US10371405B2 (en) | Building power management systems | |
Shaikh et al. | Intelligent multi-objective optimization for building energy and comfort management | |
Radhakrishnan et al. | Token based scheduling for energy management in building HVAC systems | |
Wei et al. | Deep reinforcement learning for joint datacenter and HVAC load control in distributed mixed-use buildings | |
Gruber et al. | Model-based controllers for indoor climate control in office buildings–complexity and performance evaluation | |
Zhu et al. | Online optimal control of variable refrigerant flow and variable air volume combined air conditioning system for energy saving | |
Franco et al. | A method for optimal operation of HVAC with heat pumps for reducing the energy demand of large-scale non residential buildings | |
Zhao et al. | Data-driven online energy management framework for HVAC systems: An experimental study | |
Qiu et al. | Stochastic optimized chiller operation strategy based on multi-objective optimization considering measurement uncertainty | |
Javed et al. | Experimental testing of a random neural network smart controller using a single zone test chamber | |
Lara et al. | Modeling and identification of the cooling dynamics of a tropical island hotel | |
Hou et al. | Real-time optimal control of HVAC systems: Model accuracy and optimization reward | |
Xu et al. | Robust MPC for temperature control of air-conditioning systems concerning on constraints and multitype uncertainties | |
Rodriguez et al. | Zoned heating, ventilation, and air–conditioning residential systems: A systematic review | |
Wu et al. | Optimizing demand-controlled ventilation with thermal comfort and CO2 concentrations using long short-term memory and genetic algorithm | |
Marantos et al. | Rapid prototyping of low-complexity orchestrator targeting cyberphysical systems: The smart-thermostat usecase | |
Aldakheel et al. | Indoor environmental quality evaluation of smart/artificial intelligence techniques in buildings–a review | |
Guo et al. | Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building |