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

    • 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
Drgoňa et al. Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration
Wang et al. Reinforcement learning for building controls: The opportunities and challenges
Wang et al. A state of art review on methodologies for control strategies in low energy buildings in the period from 2006 to 2016
Ma et al. Predictive control for energy efficient buildings with thermal storage: Modeling, stimulation, and experiments
Lee et al. Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices
Váňa et al. Model-based energy efficient control applied to an office building
Kusiak et al. Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method
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
Killian et al. Implementation of cooperative Fuzzy model predictive control for an energy-efficient office building
Mařík et al. Advanced HVAC control: Theory vs. reality
Ma Model predictive control for energy efficient buildings
Nguyen et al. Modelling building HVAC control strategies using a deep reinforcement learning approach
Luo et al. Controlling commercial cooling systems using reinforcement learning
Mayer et al. A branch and bound approach for building cooling supply control with hybrid model predictive control
Hou et al. Nonlinear model predictive control for the space heating system of a university building in Norway
Gao et al. Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
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
Dawood et al. Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems
Baranski et al. Distributed exergy-based simulation-assisted control of HVAC supply chains
Atam New paths toward energy-efficient buildings: A multiaspect discussion of advanced model-based control