27 results about "Hvac control" patented technology

The invention relates to the technical field of heating ventilation air conditioner control and energy conservation, and discloses an energy-saving self-adaptive control method and system for a heating ventilation air conditioner. The method comprises the following steps: acquiring power consumption data and environment data in a building to obtain initial activity state distribution; air conditioner related energy consumption characteristics are separated according to the activity state distribution; identifying a specific activity state in combination with a user behavior database; determining a target cooling capacity output range according to the activity type and the personnel density; if the range exceeds the current setting, recalculating the operation parameters in combination with the external climate data, and generating an adjustment instruction; dynamic adjustment is executed, the comfort degree is monitored, and operation feedback is obtained; and according to the feedback continuous optimization operation strategy, energy consumption optimization is realized. According to the method, air conditioner operation can be dynamically adjusted according to the real-time activity state and the external environment, and energy consumption is effectively reduced while the comfort level is guaranteed.

The invention belongs to the technical field of intelligent building and heating ventilation air conditioner control, and particularly discloses an air conditioner system look-ahead optimization control method based on rolling prediction and global collaboration, which comprises the following steps: performing preprocessing and feature construction on air conditioner system data to obtain a historical data window; inputting the window into a Transform prediction model to obtain a future prediction sequence; outputting a group of global optimal set values through an optimization strategy generator, calculating an initial set value by the optimization strategy generator by adopting an empirical formula group on the basis of a prediction sequence, and carrying out iterative solution by taking the initial set value as a starting point and based on a system total energy consumption model of a physical coupling relationship between fusion equipment, so as to obtain the global optimal set value enabling the total energy consumption of the system to be the lowest; and issuing the global optimal set value to an equipment controller for execution, and updating the Transform prediction model according to the actual operation data. The problem of large inertia lag of the air conditioning system can be solved, and accurate and reliable prospective energy efficiency optimization is achieved.

The invention discloses a central air conditioner control method and equipment based on mechanism modeling and a medium, and relates to the technical field of heating ventilation air conditioner control. The method comprises the steps of synchronously collecting environment parameters, indoor parameters and operation parameters of cold source equipment, and performing feature extraction to obtain key feature data; according to the key feature data, constructing a grey box model fusing a physical mechanism and data driving; predicting through the grey box model to obtain a comfort penalty term, a negative energy consumption term of the cold source equipment and a model error term of a predicted value and an actual value output by the grey box model; obtaining a reward function and an efficiency attenuation value according to the comfort penalty term, the negative energy consumption term and the model error term; and training and generating a control strategy by using a reinforcement learning algorithm and taking a reward function as guidance. According to the method, the gray box model fusing the physical mechanism and the data driving is constructed, and the performance attenuation coefficient of the equipment is identified on line, so that the accuracy of system state prediction and the generalization ability of the model are remarkably improved.

In some aspects, the HVAC field device described herein includes: a housing; an HVAC functional device; a non-volatile memory unit including: a first set of computer-executable instructions in a first memory partition configured to execute an operating system; and a second set of computer-executable instructions in a second memory partition configured to execute an application module, wherein the application module is configured to control operation of the HVAC functional device; a volatile memory unit; and at least one processor configured to: execute the operating system in a first memory region of the volatile memory unit and execute the application module in a second memory region of the volatile memory unit, wherein the application module is prevented from accessing the first memory region of the volatile memory unit.

An HVAC controller includes an HVAC control module, a control housing covering or enclosing at least a portion of the HVAC control module, a near field communication (NFC) module, and a communication connection connected to the HVAC control module and the NFC module. The HVAC control module includes a controller board, a controller processor, and a controller memory disposed on the controller board. The control housing has an inner surface facing the HVAC control module and an outer surface opposite the inner surface. The NFC module includes an NFC board, NFC circuitry, and an NFC antenna connected to the NFC board. The NFC module is positioned remote from the HVAC control module and proximate the inner surface or the outer surface of the control housing.

The invention discloses a real-time energy efficiency optimization control method, equipment and device for an air conditioner for a data center, and relates to the technical field of heating ventilation air conditioner control. Comprising the steps of 1, establishing a cabinet variable frequency air conditioner refrigerating capacity prediction model based on a neural network; 2, establishing a cabinet variable frequency air conditioner energy consumption prediction model; 3, constructing a target function based on Bayesian optimization to optimize the real-time energy efficiency of the cabinet variable frequency air conditioner; on the premise that the IT equipment load requirement in the cabinet is met, the cabinet air conditioning system always operates at the optimal energy efficiency point under different outdoor temperatures and IT equipment loads, and the energy saving performance and reliability of the cabinet variable frequency air conditioner are effectively improved.

The invention relates to a multi-AI intelligent agent cooperation HVAC control method and system, and relates to the technical field of intelligent manufacturing equipment industries, and the method comprises a data collection module which is used for collecting environment data, load data and equipment data of a data center; the multi-AI agent cooperation module is used for carrying out data quality analysis, environment trend prediction, security boundary check, mode recognition, energy-saving strategy making and terminal optimization through a plurality of agents based on environment and equipment data, and generating multi-dimensional decision suggestions; the decision coordination module is used for generating an optimal decision based on the multi-dimensional decision suggestions; the execution control module is used for generating a control instruction based on the optimal decision, and the control instruction is used for controlling the heating and ventilation equipment to operate; the effect evaluation module is used for evaluating the execution effect of the optimal decision; and the knowledge base management module is used for storing and retrieving the generation experience of the optimal decision, and has the advantage of realizing intelligent and autonomous energy-saving control of heating and ventilation of the data center.

An optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU) for minimizing energy usage. The solution achieved less than 2 percent differences from conventional approaches and improved computational speed from hours to seconds. The solution also demonstrated an 80% reduction in total electric usage and a 21 percent electric bill reduction considering day-ahead price time-of-use demand response signals.

The invention discloses an intelligent optimization control method of a heating and ventilation system based on a water freezing system and a dehumidifier, and relates to the technical field of heating and ventilation control. The method comprises the steps of determining a water supply temperature interval and a water supply flow interval of a cold water host; according to the to-be-optimized chilled water outlet temperature, the cooling tower outlet water temperature, the tail end demand cooling capacity, the chilled water temperature difference, the cooling water temperature difference, the to-be-optimized chilled water flow, the water pump power, the cooling tower power, the operation time, the electricity price, the steam unit price, the to-be-optimized regeneration temperature of at least one rotary wheel dehumidifier, the to-be-optimized rotary wheel rotating speed and the to-be-optimized treatment air volume, the to-be-optimized rotary wheel rotating speed of the at least one rotary wheel dehumidifier is optimized. Constructing a target function; under the constraint condition, the target function is solved based on the interval multi-target particle swarm algorithm by taking the minimum target function as the target, and target values corresponding to the to-be-optimized frozen water outlet temperature, the to-be-optimized frozen water flow, the to-be-optimized regeneration temperature, the to-be-optimized rotating wheel rotating speed and the to-be-optimized processing air volume are obtained; and controlling the heating and ventilation system by using the target value. The control efficiency of the heating and ventilation system can be improved.

This invention discloses a real-time energy efficiency optimization control method, equipment, and device for air conditioners used in data centers, relating to the field of HVAC control technology; it includes: Step 1: establishing a neural network-based prediction model for the cooling capacity of a rack-mounted variable frequency air conditioner; Step 2: establishing a prediction model for the energy consumption of a rack-mounted variable frequency air conditioner; Step 3: constructing a Bayesian optimization-based objective function for real-time energy efficiency optimization of the rack-mounted variable frequency air conditioner; This invention, while meeting the load requirements of IT equipment within the rack, ensures that the rack-mounted air conditioning system always operates at its optimal energy efficiency point under different outdoor temperatures and IT equipment loads, effectively improving the energy efficiency and reliability of the rack-mounted variable frequency air conditioner.

The invention provides an end-to-end intelligent control method and system for a heating ventilation air conditioner and a medium, and the method comprises the steps: obtaining a control task, and decomposing the control task into a high-frequency part and a low-frequency part based on a mixed edge-cloud architecture; collecting real-time data of a high-frequency part based on a local edge node, analyzing the real-time data according to a neural network model, generating heating ventilation air conditioner control data, and caching an experience tuple; based on cloud AI platform experience tuples, aggregating to form a global data set, training the deep reinforcement learning model, and performing compression processing on the trained deep reinforcement learning model to obtain a compression model adapted to local edge nodes; pushing the compression model to a corresponding local edge node in an over-the-air downloading mode based on an edge-cloud closed-loop updating mechanism; through end-to-end intelligent control, the energy consumption and the operation cost can be minimized, meanwhile, the thermal comfort degree of a user is improved, and long-term healthy operation of the heating ventilation air conditioning equipment is guaranteed.

The utility model relates to the technical field of heating, ventilating and air conditioning control, in particular to an intelligent building heating, ventilating and air conditioning control system. The system is composed of an environmental parameter acquisition module, a main controller, an actuator group and a communication module, the environmental parameter acquisition module comprises a temperature sensor, a humidity sensor and an air quality sensor which are arranged in a building space, and the main controller is integrated with a data processing module and a storage module. The actuator set comprises a frequency conversion fan, an electric control valve and a compressor. The utility model provides an intelligent building heating, ventilating and air conditioning control system which can integrate multi-dimensional environment perception, supports dynamic hierarchical control and has high expansibility communication capability, so as to solve the problems of extensive environment regulation and control, low energy efficiency ratio, insufficient man-machine interaction and the like in the prior art.

The present application relates to the field of intelligent regulation and energy saving technology of hospital environment air quality, in particular to a big data management and intelligent evaluation system for hospital environment air quality; comprising multi-modal data acquisition, data processing, dynamic transmission modeling, dynamic Bayesian network risk modeling and forward-looking risk hedging and energy optimization modules; the system collects environment and people flow data, calculates dynamic air transmission coefficient, and constructs a dynamic Bayesian network model to calculate the cross-zone transmission risk probability; the core is that when the risk probability exceeds the threshold value, the system will actively solve the multi-objective optimization problem with the goal of minimizing energy consumption to generate optimal HVAC control instructions; the present application realizes the change from lagging evaluation to forward-looking risk hedging, and can actively identify and regulate the risk before the pollution exceeds the standard.

An example HVAC system includes first and second HVAC components. A first HVAC control is configured to control the first HVAC component according to one or more first HVAC system configuration parameters. The first HVAC component comprises an indoor HVAC component. The second HVAC component comprises an outdoor HVAC component. The HVAC system is configured such that one or more component properties and / or configuration parameters of the outdoor HVAC component are obtainable via a mobile device. The one or more component properties and / or configuration parameters of the outdoor HVAC component are usable for setting up the first HVAC control to control the indoor HVAC component in accordance with the one or more component properties and / or configuration parameters of the outdoor HVAC component.

A system for smart HVAC controllers that is capable of automatically detecting and handling all HVAC control wires, whether the HVAC system is a single or dual transformer system. The system comprises a channel selector for selecting the various channels of an HVAC system for detection, one or more voltage and current limiters for conditioning channel signals for input to a microcontroller unit, a microcontroller comprising an intelligent detection algorithm for operating the channel selector and detecting and handling channel signals, and a one-way gate for protecting against damage from connection of two transformers during wire detection.

We will improve RL-based AC / HVAC controllers, particularly in terms of their pre-training. [Solution] The present invention relates to a method for determining at least one simulation parameter of a simulation for pre-training a reinforcement learning (RL) based controller (14) for an air conditioning (AC) system or a heating, ventilation and air conditioning (HVAC) system (10). The present invention also relates to a system (100) for determining at least one simulation parameter of a simulation for pre-training an RL-based controller (14) for an AC system or a HVAC system (10). Furthermore, the present invention also relates to an RL-based controller (14).

A system and method for managing HVAC components of a building are disclosed. An example computer-implemented method includes: collecting and analyzing sensor data from sensors in the building; determining, based on the analytics of the sensor data, a plurality of macro parameter values for heating, cooling and ventilation for the building; generating, based on the plurality of macro parameter values, micro parameter values comprising a plurality of forecast values for at least one command point of the HVAC components of the building; and instructing the HVAC components on how to operate using the micro parameter values. An artificial intelligence engine may be implemented to predict operating values for the HVAC components based on the macro or micro parameter values.

A heating, ventilation, and air conditioning (HVAC) control device comprises a network interface and a processor operably coupled to one another. The processor is configured to collect event data for a state transition within the HVAC control device, electronically calculate, based on first state conditions corresponding to the first state, a first compensation value, assign a first dynamic weighting modifier to the first compensation value, electronically calculate, based on second state conditions corresponding to the second state, a second compensation value, and assign a second dynamic weighting modifier to the second compensation value. Further, the processor is configured to electronically combine a weighted version of the first compensation value and a weighted version of the second compensation value into a sensed temperature value and trigger a demand cycle in an HVAC system to match the sensed temperature value in a space to a set point temperature.

Methods and systems for generating parameters and loading them into an HVAC controller are described herein. Setpoint functions are input into a simulation of an environment of the controller. An output of the controller within the simulation is evaluated, using an objective function based on oscillations of the output of the controller, to determine the parameters, which are then loaded into the controller. Alternatively or additionally, data corresponding to an environment in which the controller is implemented is received. The data includes information about a setpoint signal received from an external controller and feedback from a physical system controlled by the controller. The data is evaluated using a reward function to determine parameters, which are then loaded into, or used to augment existing parameters within, the controller. By using the techniques herein, the controller may be configured to stabilize the setpoint signal received from the external controller.

The application provides a heating ventilation air conditioning control method and system based on dynamic heat load prediction, a device and a storage medium. The method comprises the following steps: acquiring individual physiological data, environmental parameters and regional personnel distribution data in real time; determining the real-time individual metabolic rate equivalent based on the individual physiological data and the environmental parameters; aggregating the individual metabolic rate equivalent according to the regional personnel distribution data, determining the total heat load and the total heat load change rate of each region in the room, and decomposing the total heat load into sensible heat load and latent heat load; and generating the supply air temperature, supply air volume and humidity control instructions of the heating ventilation air conditioning system for feedforward regulation based on the sensible heat load, the latent heat load and the total heat load change rate. The technical scheme of the application can realize dynamic heat load prediction based on individual physiological data and environmental parameters, quantize the total heat load in the room in real time, and generate feedforward regulation control instructions of the heating ventilation air conditioning system.

This invention relates to the field of HVAC control equipment technology and discloses a wired controller that satisfies multiple wiring methods, including a front shell, a middle and rear cover, and a rear cover. The middle and rear cover are stacked on the rear side of the front shell, and the rear cover is stacked on the side of the middle and rear cover opposite to the front shell. The middle and rear cover has a first notch, and a wire groove extending outward from the first notch is provided on the side of the middle and rear cover opposite to the front shell. The side wall of the front shell has a wire exit notch, and the end of the wire groove away from the first notch corresponds to the position of the wire exit notch. The rear cover has a second notch, and the second notch corresponds to the position of the first notch. By setting the first notch of the middle and rear cover and the wire groove to cooperate with the second notch of the rear cover, this invention allows the connecting wire to flexibly choose to exit along the wire groove of the middle and rear cover through the tail of the wire exit notch, or directly pass through the middle of the notch of the rear cover and hide the wire exit, thereby meeting the needs of various installation scenarios such as surface mounting and concealed mounting.

The application discloses a comprehensive energy HVAC control method based on deep reinforcement learning, and has the characteristics that the method comprises the following steps: firstly, determining the control action space that can be taken by the HVAC system; then, collecting the state data of the building HVAC system; then, constructing a control sequence, updating the HVAC control action by a DRL algorithm; calculating the immediate reward and the maximum cumulative reward after the HVAC system takes the action; finally, approximating the Q value by using an artificial neural network, and selecting the optimal control action. The application expresses the HVAC operation process as a Markov decision process, develops the HVAC control method based on DRL, minimizes the building energy cost and maintains the comfortable temperature of the residents, further proposes a heuristic method for the effective control of a complex multi-zone system in order to obtain higher expansibility, and effectively reduces the building energy cost.

Disclosed is a containerized heating, ventilation, and air-conditioning (HVAC) system comprising an HVAC unit and one or more ducts from the HVAC unit to an equipment rack. The ducts prevent mixing between the fresh and exhaust airflow, thus improving efficiency. Sensors located at sources of heat generating equipment within the racks may be used by controllers to monitor temperatures of the components at the source of heat generation, typically at the highest temperatures. The temperatures may be aggregated to determine the temperatures of devices, modules, racks, and the container interior cavity. Dampers on the ducts, at the rack inlets, at the module inlets, at the devices inlets, and such may assist in regulating airflow preferentially to the hottest components, devices, modules, or racks.

A wireless thermostat may be associated with a user account of an external web service that may facilitate remote access and / or control of the wireless thermostat. A remote device may be used to access a user's account hosted by the external web service. The wireless thermostat may be identified to the external web service and associated with the user's account by one or more unique identifiers previously delivered to the remote device from the wireless thermostat.

This invention relates to the field of HVAC control technology, and particularly to a cloud-based collaborative configuration and distribution system and method for HVAC gateway parameters. The system includes a cloud server, a mobile device terminal, a site survey gateway device, and a field-installed gateway device. The mobile device terminal can communicate with the site survey gateway device and is equipped with a mini-program that allows for parameter configuration and debugging of the site survey gateway device. The configured communication parameters are then uploaded to the cloud server for storage. The mobile device terminal can also retrieve the saved communication parameters from the cloud server and distribute them to the field-installed gateway device. This invention, through the collaborative work of its components, achieves one-time parameter configuration and cloud synchronization during the site survey phase. During the construction phase, no secondary on-site visits by technical personnel are required; construction personnel can distribute parameters through lightweight operations, ensuring parameter configuration accuracy, reducing labor costs, shortening the construction cycle, and improving project implementation efficiency.

The present application relates to the technical field of smart home and HVAC control, and particularly relates to an air conditioner micro controller. The air conditioner micro controller comprises a controller body and a shell, and the controller body comprises a main control module, a touch display module, a communication module, a sensor interface module and an output module. The main control module comprises a first processing core unit and a second processing core unit, and the first processing core unit is in communication connection with the touch display module. The sensor interface module, the communication module and the output module are all connected with the second processing core unit. The second processing core unit communicates with a communication device through the communication module, and is used for receiving transmission data of the communication device and an environmental parameter acquisition sensor, and outputting a target control signal based on the transmission data and a preset control logic. The technical scheme of the present application can solve the technical problems of single function, unintelligent control, high energy consumption and inconvenient management in the prior art.

Systems and methods for a centrally designed vehicle HVAC system that cools vehicle components as well as the cabin and / or delivery containers. The HVAC system is designed with the ability to divert HVAC capacity from one vehicle system to another, allowing for more optimized HVAC usage. In some examples, the HVAC system disclosed herein allows for a more optimized HVAC system size, including decreased HVAC system weight and power consumption. In various implementations, the HVAC system uses back office prediction and remote control to make the determination of HVAC system bias based on current weather conditions as well as forecasted weather conditions. The HVAC system provides a comfortable vehicle cabin while ensuring the thermal condition of vehicle systems and components are managed to avoid interruptions to service.