Nothing Special   »   [go: up one dir, main page]

Zhang et al., 2019 - Google Patents

Building HVAC scheduling using reinforcement learning via neural network based model approximation

Zhang 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 …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00Systems controlled by a computer
    • G05B15/02Systems controlled by a computer electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING ENGINES OR PUMPS
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING, AIR-HUMIDIFICATION, VENTILATION, USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety systems or apparatus
    • F24F11/0009Electrical control or safety systems or apparatus
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00Control of temperature
    • G05D23/19Control 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