Zhang et al., 2019 - Google Patents
Building HVAC scheduling using reinforcement learning via neural network based model approximationZhang et al., 2019
View PDF- Document ID
- 10514806151798329882
- Author
- Zhang C
- Kuppannagari S
- Kannan R
- Prasanna V
- Publication year
- Publication venue
- Proceedings of the 6th ACM international conference on systems for energy-efficient buildings, cities, and transportation
External Links
Snippet
Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to maintain thermal comfort and indoor air quality (IAQ), account for almost half of the energy …
- 230000001537 neural 0 title description 31
Classifications
-
- 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
- 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
-
- 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
- G06Q10/063—Operations research or analysis
-
- 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
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems 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
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- 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"
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING ENGINES OR PUMPS
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING, AIR-HUMIDIFICATION, VENTILATION, USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety systems or apparatus
- F24F11/0009—Electrical control or safety systems or apparatus
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Building HVAC scheduling using reinforcement learning via neural network based model approximation | |
Yao et al. | State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field | |
Taheri et al. | Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review | |
Yang et al. | Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control | |
Wang et al. | Reinforcement learning for building controls: The opportunities and challenges | |
Ma et al. | Predictive control for energy efficient buildings with thermal storage: Modeling, stimulation, and experiments | |
Mařík et al. | Advanced HVAC control: Theory vs. reality | |
Lee et al. | Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices | |
Kusiak et al. | Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method | |
Váňa et al. | Model-based energy efficient control applied to an office building | |
Parisio et al. | Implementation of a scenario-based mpc for hvac systems: an experimental case study | |
Radhakrishnan et al. | Learning-based hierarchical distributed HVAC scheduling with operational constraints | |
Ma | Model predictive control for energy efficient buildings | |
Mayer et al. | A branch and bound approach for building cooling supply control with hybrid model predictive control | |
Luo et al. | Controlling commercial cooling systems using reinforcement learning | |
O'Dwyer et al. | Prioritised objectives for model predictive control of building heating systems | |
Hou et al. | Nonlinear model predictive control for the space heating system of a university building in Norway | |
Ferhatbegović et al. | Model based predictive control for a solar-thermal system | |
Kannan et al. | Energy management strategy for zone cooling load demand reduction in commercial buildings: A data-driven approach | |
Baranski et al. | Distributed exergy-based simulation-assisted control of HVAC supply chains | |
Nguyen et al. | Modelling building HVAC control strategies using a deep reinforcement learning approach | |
Jain et al. | Data predictive control for peak power reduction | |
Raftery et al. | A new control strategy for high thermal mass radiant systems | |
Atam | New paths toward energy-efficient buildings: A multiaspect discussion of advanced model-based control | |
Berouine et al. | A predictive control approach for thermal energy management in buildings |