49 results about "Hvac control" patented technology

The invention discloses a building heating, ventilating and air conditioning control method with dynamic energy efficiency optimization, and belongs to the technical field of heating, ventilating and air conditioning control. The method comprises the following steps: step 1, acquiring temperature, humidity, carbon dioxide concentration and personnel distribution data in real time, and performing preliminary data processing through an edge computing node; step 2, realizing accurate prediction of future load change of the building; step 3, generating an optimal control strategy by using a reinforcement learning algorithm, and realizing dynamic optimization control of equipment operation; 4, the compliance of equipment operation, network communication and control instructions is monitored in real time, and a closed-loop dynamic tuning mechanism is constructed to continuously optimize control strategy parameters; step 5, dividing a control task into a group control layer, a region layer and a terminal layer, and establishing communication among the layers through a standardized interface so as to realize accurate transmission and execution of an instruction; and step 6, designing a friendly human-computer interaction interface, displaying a building operation state, energy consumption analysis and a control strategy, and supporting multi-terminal access and remote monitoring.

The invention provides a heating ventilation air conditioner control method, system and device based on dynamic heat load prediction and a storage medium. The method comprises the following steps: acquiring indoor individual physiological data, environmental parameters and regional personnel distribution data in real time; determining a real-time individual metabolic rate equivalent based on the individual physiological data and the environmental parameters; aggregating the individual metabolic rate equivalents according to the regional personnel distribution data, determining the total heat load and the total heat load change rate of each indoor region, and decomposing the total heat load into a sensible heat load and a latent heat load; and on the basis of the sensible heat load, the latent heat load and the total heat load change rate, air supply temperature, air supply amount and humidity control instructions of the heating ventilation air conditioning system for feed-forward adjustment are generated. According to the technical scheme, dynamic thermal load prediction based on individual physiological data and environmental parameters can be achieved, the indoor total thermal load is quantified in real time, and the feedforward adjustment control instruction of the heating ventilation air conditioning system is generated.

The invention relates to the technical field of heating, ventilation and air conditioning control, in particular to an intelligent laboratory air quality control system and method.The intelligent laboratory air quality control system comprises a data acquisition module, an intelligent decision module, a fault diagnosis module and an execution control module; meanwhile, feedback type multi-target collaborative operation parameters are generated through a plurality of heterogeneous strategy models; the fault diagnosis module analyzes equipment health data through an AI health degree baseline model, and outputs diagnosis information as a dynamic constraint; and the execution control module fuses the compensation control sequence, the cooperative parameters and the diagnosis information, generates a final physical control instruction verified by a safety boundary rule, and drives a heating ventilation air conditioner execution mechanism. According to the invention, through a control strategy integrating prediction, optimization and diagnosis, the stability of the laboratory environment, the operation economy and the long-term operation reliability of the system are significantly improved.

Optimal control of multiple heating, ventilation, and air-conditioning units in an open-plan space demands fast and accurate thermodynamic modeling. Prior methods lack scalability required for effective control in large open-plan offices primarily due to air-mixing interactions. The present disclosure describes a physics-informed graph neural network (PI-GNN) to overcome these challenges. Specifically, thermodynamic interactions are modeled as edges between nodes that represent cells. Further, a modeling approach is used that allows explicit modeling of wall and window surface temperatures which are commonly ignored. The method of present disclosure utilizes PI-GNN as a state-estimator that employs a receding-horizon approach for optimal HVAC control. PI-GNNs are adapted for building HVAC control by incorporating a time-resetting strategy to handle time-dependent ambient conditions and therefore set-points. The method of the present disclosure outperforms a regular PINN model and other baseline control strategies on thermal model accuracy, computation time, energy consumption, and user comfort.

The invention relates to the technical field of intelligent regulation and control and energy conservation of hospital environment air quality, in particular to a big data management and intelligent evaluation system for the hospital environment air quality. The system comprises a multi-modal data acquisition module, a data processing module, a dynamic transmission modeling module, a dynamic Bayesian network risk modeling module and a prospective risk hedging and energy consumption optimization module. The system calculates a dynamic air transmission coefficient by collecting environment and people flow data, and constructs a dynamic Bayesian network model to calculate a cross-region propagation risk probability; the method is characterized in that when the risk probability exceeds a threshold value, a system actively solves a multi-objective optimization problem with minimization of energy consumption as an objective, and an optimal HVAC control instruction is generated; according to the method, the conversion from lagging evaluation to prospective risk hedging is realized, and the risk can be actively identified and regulated before the pollution exceeds the standard.

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 present application relates to the HVAC control technical field, specifically to a kind of intelligent laboratory air quality control system and method, including data acquisition module, intelligent decision module, fault diagnosis module and execution control module, intelligent decision module is based on disturbance data generation feedforward compensation control sequence, simultaneously through multiple heterogeneous strategy model generates feedback multi-objective collaborative operation parameter;Fault diagnosis module passes through AI health degree baseline model analysis equipment health data, exports diagnostic information as dynamic constraint;Execution control module fuses the above-mentioned compensation control sequence, collaborative parameter and diagnostic information, generates the final physical control instruction verified by safety boundary rule, drives HVAC actuator.The present application is fused by prediction, optimization and diagnosis control strategy, significantly improves the stability of laboratory environment, operating economy and the reliability of long-term operation of system.

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 a regulation and control strategy planning method and system for a heating ventilation air conditioner. The method comprises the following steps that a near-end strategy optimization PPO algorithm is adopted to construct an air conditioner strategy optimization network model; constructing an air conditioner thermal model and an air conditioner electricity charge prediction model; the obtained environment parameters, time parameters and air conditioner parameters are input into an air conditioner thermal model for indoor temperature prediction; based on the indoor temperature prediction value at the t + 1 moment and an air conditioner electricity charge prediction model, an air conditioner strategy optimization network model is trained, so that an air conditioner regulation and control strategy output by the air conditioner strategy optimization network model is converged; and collecting real-time environment parameters, and inputting the real-time environment parameters into the trained air conditioner strategy optimization network model for air conditioner regulation and control strategy planning. The method solves the problems that an existing heating ventilation air conditioner regulation and control method only takes indoor temperature as an optimization target and lacks quantitative management of power consumption cost, so that a system cannot adaptively adjust a strategy according to peak and valley electricity prices of a power grid, and conditions such as continuous high-power operation occur in the peak period of power consumption occur.

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.

A substantially tubeless and wireless system accurately measures differential air pressures at several different indoor points relative to outside air. In some embodiments, individual units (e.g., receivers and / or zone transmitters) apply an algorithm to derive the air pressure in the space in which the unit is installed. If the derived pressure is at or near the outside pressure, the unit starts a counter to measure the length of time the equalized pressure condition exists. If the condition persists longer than the time it takes to egress or ingress through a door or to open and close a window, a flag signal is sent to the unit. The unit then takes action to halt cooling or heating in the space. In some embodiments, the unit may be a thermostat, or HVAC controlling device or cloud acting as a HVAC controller. Notification to the end user of the action taken may be provided on the thermostat panel or through a mobile application or other available source.

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.

In an exemplary system embodiment, a mobile device is configured to receive and / or determine a first identifier of the first HVAC control and to automatically configure one or more first settings for the first HVAC control corresponding with the first identifier. The one or more first settings are stored within and retrievable directly from the memory of the mobile device. The mobile device is also configured to receive and / or determine a second identifier of the second HVAC control and to automatically configure one or more second settings for the second HVAC control corresponding with the second identifier. The one or more second settings are stored within and retrievable directly from the memory of the mobile device for wireless transmission to the second HVAC control for download to a memory of the second HVAC control for controlling at least one HVAC component according to the one or more second settings.

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.

This invention discloses a method for HVAC control in highway service areas based on the Internet of Things (IoT) and neural networks. The method includes: dividing the building area of ​​the target service area into functional zones; deploying IoT sensors in each functional zone to acquire environmental data, HVAC equipment parameters, and pedestrian flow distribution data; collecting vehicle information at the entrances and exits of the target service area and the number of people entering and exiting each functional zone; using the pedestrian flow distribution data, vehicle information, and number of people entering and exiting the current service area and the previous service area as a first dataset, inputting it into a pedestrian flow prediction neural network to generate corresponding pedestrian flow prediction data; using the environmental data, HVAC equipment parameters, pedestrian flow distribution data, and pedestrian flow prediction data of the current service area as a second dataset, inputting it into a control strategy neural network to output control strategies for each group of HVAC equipment in the current service area within a future preset time period; and controlling each group of HVAC equipment within the future preset time period according to the control strategies.

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).

The application belongs to the field of heating ventilation air conditioning, and proposes a heating ventilation air conditioning control method based on a random probability weighted composite sampling strategy, and the working process is as follows: step 101, an intelligent agent is constructed and initialized, and the initial exploration rate is set to 1; step 102, the state of the current heating ventilation air conditioning system is acquired, and whether the current exploration rate is less than the set value of starting to use probability weighted sampling is judged; step 103, a random probability weighted composite sampling strategy is used to select an action; step 104, a control instruction corresponding to the action is executed, and a Q table is updated; step 105, the exploration rate is reduced with a training setting period; step 106, whether the intelligent agent training reaches an end standard is judged, if not, the training is continued, otherwise, state parameters are input into the trained intelligent agent. The method can effectively improve the performance of the heating ventilation air conditioning system, reduce the operation cost, and enhance the stability of the system, and has certain application prospect.

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.

The application relates to a building heating control method and device and a storage medium, and belongs to the technical field of building control. The method comprises the following steps: acquiring air conditioner basic information corresponding to each air conditioner in a building and environment information of an area where the air conditioner is located; the air conditioner basic information comprises an air conditioner running state and an air conditioner type; the environment information comprises passenger flow data and lighting lamp data; whether the air conditioner has an energy consumption abnormality is judged based on the passenger flow data, the lighting lamp data, the air conditioner type and the air conditioner running state; if the energy consumption abnormality exists, it is determined that the air conditioner has a first abnormality; energy consumption prompt information is generated according to position information of the air conditioner and is sent to a management terminal, and the air conditioner is regulated and controlled according to a preset air conditioner adjustment strategy. The application has the effects of reducing energy waste of a building heating air conditioner and realizing energy-saving intelligent management of the building heating air conditioner.

The invention discloses an intelligent regulation and control composite device and method for a heating ventilation air conditioner of a public building, the intelligent regulation and control composite device comprises a building body and a regulation and control composite device body, the regulation and control composite device body is placed in the building body, a suspended ceiling is installed at the top of a floor of the building body, and a composite sensor array is arranged on the suspended ceiling; a plurality of control modules are placed in the regulation and control composite device body, the control modules are used for regulating and controlling the heating, ventilation and air conditioning equipment, and a cooling mechanism is arranged below the control modules. According to the intelligent regulation and control composite device and method for the heating ventilation air conditioner of the public building, the control module is placed on the cooling plate, when the temperature is high, cooling water flowing in the cooling plate conducts heat exchange on heat on the surface of the control module, and therefore cooling can be rapidly conducted; and in addition, the cooling plate is connected with the cooling water supply assembly through the quick plug, so that mounting and dismounting are convenient.