972 results about "HVAC" patented technology

A heat pump system for a vehicle may efficiently adjust the temperature of heating elements by using a chiller where a refrigerant and a coolant exchange heat. At the same time, the heat pump system may efficiently perform cooling and heating of a rear seat by using a rear heating, ventilation, and air conditioning (HVAC) module provided with a heat-exchanger.

The invention relates to the field of heating ventilation air conditioner automatic control, and discloses an efficient central air conditioner cold station optimization control system and method based on physical AI. The system comprises a data acquisition module, an energy efficiency modeling module, a load prediction module, a rolling optimization module, an execution control module and a feedback correction module. The data acquisition module forms a time sequence data set; the energy efficiency modeling module adopts transfer learning to obtain a system energy efficiency model of a target domain cold station; the load prediction module outputs a predicted cold load sequence; the rolling optimization module is used for solving an optimal control sequence under the constraints of cooling capacity balance, an equipment operation boundary and a temperature difference threshold by taking a predicted cooling load sequence, a system energy efficiency model and a current equipment operation state as input under a model prediction control framework; the execution control module issues a first control action to control the water supply temperature, the water pump and fan frequency and unit start and stop; and the feedback correction module calculates a residual error and is used for closed-loop correction of the next period.

The invention relates to the technical field of building intelligent control and energy saving, and discloses a building equipment intelligent control optimization management system based on a green low-carbon building. The system comprises five subsystems, wherein an environmental parameter sensing subsystem is used for acquiring temperature and humidity gradients, illumination intensity distribution and personnel activity thermodynamic diagram data inside and outside a building through a distributed sensing network to form a multi-dimensional environmental state vector; the equipment operation state acquisition subsystem acquires an operation power curve, an energy efficiency conversion rate curve and an equipment health degree index of heating ventilation air conditioning, illumination and renewable energy equipment in real time; the dynamic load prediction subsystem establishes an energy consumption load space-time distribution prediction model according to historical data time sequence relevance; the control strategy generation subsystem is used for generating an optimized instruction set containing an equipment start-stop time sequence, a power regulation gradient and an energy distribution weight in combination with the current environment vector and a predicted load; the execution terminal adapts the subsystem to convert the instruction into a device compatible signal and calibrate a response delay.

An infection control tool for a building HVAC system includes processing circuitry configured to receive one or more inputs defining a HVAC system configuration for each of a baseline scenario and one or more other scenarios. The processing circuitry is also configured to estimate infection risk and at least one of energy consumption or operating cost for each of the one or more other scenarios relative to the baseline scenario. The processing circuitry is also configured to operate a display to provide the infection risk and at least one of the energy consumption or operating cost to a user.

A building-integrated artificial intelligence system forming a continuous ambient neural field is disclosed. The system includes a distributed multimodal sensor lattice, an on-premise symbolic cognition engine, and an adaptive environmental control kernel operating entirely at the building edge without reliance on external cloud services. Sensor data from optical, thermal, acoustic, airflow, pressure, structural, electrical, and chemical modalities are transformed into non-identifying occupancy vectors, behavioral glyphs, risk indicators, and environmental state descriptors. A privacy-governed policy graph determines sensor permissions, redaction thresholds, consent conditions, emergency overrides, and jurisdiction-specific compliance parameters. The neural field predicts occupancy loads, optimizes HVAC, ventilation, and lighting, detects accidents and structural anomalies, classifies emergent risks, and generates redacted event capsules for audit and emergency dispatch. A federated topology enables multiple buildings to exchange compressed symbolic templates to improve predictive accuracy without transmitting raw data. The system provides a universal, regulation-aligned AI nervous system for autonomous building operations.

Mobile, expandable cleanroom systems, methods, and devices are provided in accordance with various embodiments. Various embodiments include an expandable cleanroom that may include one or more tent enclosures and one or more door assemblies. Some embodiments include one or more HVAC systems. Some embodiments include a raisable truss structure that may lift and support the one or more tent enclosures. The expandable cleanroom may be coupled with an antechamber and / or a mechanical room. The combination of expandable cleanroom, antechamber, and / or mechanical room may be based on the form of a shipping container that may be transported by flatbed truck, railcar, boat, or other vehicles. This May allow for rapid deployment along with avoiding the need for wide-load permits; the various embodiments may also provide for inconspicuous deployment.

The invention relates to a variable dew point control method of an all-air air conditioning system, belongs to the technical field of heating ventilation air conditioners, and solves the technical problems of temperature and humidity coupling in the prior art and difficulty in landing of a chilled water temperature resetting technology. The control method comprises the steps of S1, parameter initialization, S2, system starting and stabilizing, S3, air supply dew point dynamic adjustment and cold water valve control, S4, temperature and air supply amount control and S5, fresh air optimization and air valve control. The water supply temperature is adjusted by dynamically adjusting the air supply dew point and dynamically increasing the temperature of the outlet of the surface air cooler, the energy efficiency of the unit is remarkably improved, and the system is suitable for the chilled water temperature resetting technology and compatible with the application of the medium-temperature water supply technology.

The invention discloses a heating, ventilation and chilled water cooperative control method and system based on a double-branch deep and shallow feature fusion neural network, and the method comprises the steps: obtaining and preprocessing the historical operation data of a heating, ventilation and chilled water system, and forming standardized training data; a double-branch deep and shallow feature fusion neural network model is trained based on the standardized training data, and the model realizes equipment collaborative prediction through a cold water host load prediction branch and a freezing pump frequency prediction branch in combination with a deep and shallow feature fusion mechanism; acquiring environmental parameters in real time, inputting the environmental parameters into the trained model, and outputting a cold water host load predicted value and a freezing pump frequency predicted value; and correcting the predicted value based on a physical constraint correction algorithm, and outputting an optimization control instruction. The technical problem of poor control reliability caused by insufficient equipment collaboration, insufficient feature extraction and lack of physical constraints in the prior art is solved.

The invention relates to the technical field of energy-saving control, and discloses a self-adaptive energy-saving control method and system for building heating and ventilation equipment based on digital twinning. The method comprises the steps of collecting temperature and humidity in a building, heating and ventilation equipment operation, external environment and personnel flow data, extracting energy consumption fluctuation characteristics and performing coupling analysis on the energy consumption fluctuation characteristics and equipment operation data. A distributed lag regression model is constructed in combination with external environment data, the environment influence weight and trend are calculated, and a high-dynamic region is identified through fuzzy reasoning. Energy consumption is predicted based on high-dynamic area data, a waste value is calculated, equipment operation parameters are optimized, and a target power sequence is generated through population iteration. An adjusting instruction is generated through analysis and deployed to a control system, thermal sensation feedback and instantaneous power data are collected, comfort evaluation is conducted, and the adjusting instruction is updated for optimization control. According to the method, dynamic balance of energy conservation and comfort can be realized.

A HVAC&R system includes a heat pump configured to selectively circulate a working fluid. The heat pump includes an outdoor heat exchanger, an indoor heat exchanger, a compressor, a reversing valve configured to direct the working fluid from the compressor to the outdoor heat exchanger or to the indoor heat exchanger, and a controller. The controller is configured to determine whether a pressure of the working fluid exiting the compressor exceeds a pressure threshold, and, in response to determining that the pressure of the working fluid exiting the compressor exceeds the pressure threshold, control the reversing valve to block the working fluid from flowing from the compressor through the reversing valve to the outdoor heat exchanger.

The invention discloses a building air conditioner energy consumption-electric charge-comfort level intelligent control method based on MMOE and ParetoMTL, and the method comprises the following steps: (1) collecting the cooling load data of a large commercial building air conditioner, carrying out the abnormal value detection, and completing the normalization preprocessing; (2) constructing an environment-price-behavior multi-source time sequence data set and performing grading standardization; (3) dividing a training set, a verification set and a test set according to building grouping; (4) completing feature fusion and realizing task decoupling under cooperative constraint by using an MMOE mechanism; (5) jointly training a three-branch expert network, an MLP gating network and a PCGrad shared parameter optimization model; (6) generating a multi-task prediction and Pareto solution set on the test set; and (7) evaluating prediction performance and trade-off efficiency by using a plurality of indexes. According to the method, multi-target conflict, physical consistency and Pareto solution set missing difficulty can be effectively solved, and a new scheme is provided for demand response HVAC intelligent control.

The invention provides a noise isolation type heat energy transfer heating method and system, and belongs to the technical field of industrial waste heat utilization and heating ventilation air conditioning. The system comprises a waste heat fluid collecting unit, a first-stage heat exchanger unit, a middle heat transfer medium circulating unit, a second-stage heat exchanger unit and a heating circulating water conveying unit which are sequentially connected through pipelines. The system further comprises an acoustic isolation structure unit, a temperature regulation and control unit and a system supporting frame unit which are coupled with the thermal unit structure. The core of the system is that through thermal-acoustic integrated collaborative design, waste heat of heat source equipment is efficiently transferred, meanwhile, effective isolation of overall operation noise of the system is achieved through a multi-mode and multi-physical-field noise suppression mechanism, the technical contradiction that heat energy recovery and noise control are separated mutually in the prior art is solved, and the system is suitable for large-scale popularization and application. And the device is particularly suitable for heating places with strict requirements on sound environments.

An assembly includes a fluid conditioning system including a cascade module and an active charge compensator integrated into the fluid conditioning system. The fluid conditioning system is transformable between a first mode and a second mode. In the first mode, the fluid conditioning system includes a first vapor compression loop and a second vapor compression loop. The first vapor compression loop and the second vapor compression loop are thermally coupled at the cascade module and the second vapor compression loop is fluidly separate from the first vapor compression loop. In the second mode, the fluid conditioning system includes a single vapor compression loop. The active charge compensator is operable to control a working fluid charge within the fluid conditioning system based on the mode of operation selected from the plurality of modes.

The invention relates to the technical field of building energy consumption management, in particular to a building heating ventilation air conditioner intelligent control method for energy efficiency optimization, and the method comprises the steps: dividing a building into a plurality of independent temperature control regions, and deploying multifunctional sensor nodes; acquiring real-time environment data, personnel data and weather forecast data; the ultra-short-term load prediction model is maintained to predict the cold / heat load demand of the area in the future 15 minutes, and a cold / heat load demand prediction value is obtained; reporting the cold / heat load demand prediction value to a central processing unit in a standardized data format; the central processing unit optimizes the energy distribution of the whole building through a multi-objective optimization algorithm based on the prediction requirements of all the regions, and obtains a resource scheduling list in the next 15-minute period; and according to the resource scheduling list, a regulation and control instruction is issued to equipment in each independent temperature control area. Therefore, the problems of response lag, extensive control and the like in the prior art are solved.

A system includes: a climate-controlled bench; a retractable shade positioned near the climate-controlled bench and including multiple foot supports coupled to the climate-controlled bench; and at least one tablet holder attached to a component of the climate-controlled bench. In some embodiments, the system instead, or in addition, includes at least one HVAC unit and at least one heater box, both of which are thermally and / or fluidly coupled to at least one other component of the system.

Smart home devices may use a technique known as “power stealing” in order to steal power from an external environmental system. For example, thermostats may steal power from an HVAC system. Different algorithms and techniques may be used for efficiently stealing power from the HVAC system, each of which may provide different levels of power to the thermostat at different times. The smart home device may test an external system to determine which power stealing methods are compatible and calibrate various power stealing parameters. A calibration routine may sample at a plurality of discrete intervals while increasing a test load to determine a maximum current limit and an optimal power stealing method.

The invention discloses an air conditioner filter screen cleaning prompting method based on artificial intelligence, and relates to the technical field of intelligent maintenance of air conditioners and heating ventilation air conditioning equipment. Systematic interference of high humidity and accumulated water of a pressure taking channel on pressure difference reading is eliminated on the sensing level by introducing a dew point driven condensation risk grading and feature weight descending mechanism; the pressure difference indication is closer to the real resistance evolution; then, differential pressure, fan electric parameters and indoor and outdoor temperature and humidity are comprehensively utilized through multi-time-window preprocessing and a time sequence classification model, distinguishing of a dry dust blocking mode and a wet mold blocking mode is achieved, and the complex process of slow accumulation-intermittent fluctuation-seasonal migration in a coastal scene can be covered; and a hysteresis and anti-shake strategy is adopted in a decision-making level, and a minimum prompt interval is set, so that false alarm and frequent reminding caused by transient disturbance are inhibited, and the user experience is remarkably improved.

Systems and methods are provided for determining, by processing circuitry, a vehicle occupant has left a cabin of a vehicle (or that the cabin is unoccupied), where the vehicle comprises a heating, ventilation, and air conditioning (HVAC) system. In response to determining the vehicle occupant has left the cabin (or the cabin is unoccupied), the processing circuitry may adjust a parameter of the HVAC system.

The invention discloses a two-stage air conditioning system fault diagnosis method and system based on an improved deep residual network, and relates to the technical field of air conditioners. The method comprises the following steps: collecting historical operation data of equipment and parts of the heating ventilation air-conditioning system, and obtaining normal samples and fault samples; after the samples are preprocessed, labels are set for the samples according to categories, and normal-category samples and multi-category fault samples are obtained; constructing a deep residual network model fusing long-tail learning and an attention mechanism, inputting normal samples and various fault samples for training, outputting a predicted value of a data category label and determining a predicted fault type, thereby obtaining a trained heating ventilation air-conditioning system fault diagnosis model; and acquiring actual operation data, inputting the actual operation data into the trained heating ventilation air-conditioning system fault diagnosis model to carry out two-stage fault diagnosis, and outputting a fault type. According to the invention, rapid detection and accurate positioning of the fault of the heating ventilation air-conditioning system in a high imbalance data scene are realized.

The invention discloses a method for automatically modifying control codes of a heating ventilation and air conditioning system based on a large language model. The method comprises the following steps: receiving an instruction and a standardized file sent by a user; building a big language model cue word through the standardized file, and generating a model building code through a big language model; the control code grammar is verified, and digital twinborn simulation is carried out after verification is passed; analyzing a digital twinning simulation result by a large language model, and analyzing whether a code is correct or not; constructing cue words to guide the large language model to propose a code modification direction; constructing cue words, and guiding the large language model to modify error codes; and summarizing the code passing the verification and the code not passing the verification as a document and feeding back the document to a user.

The invention discloses a heating and ventilation system carbon emission prediction and scheduling control system oriented to an urban-level public building group, and relates to the technical field of heating and ventilation air conditioning system control. According to the heating and ventilation system carbon emission prediction and scheduling control system oriented to the city-level public building group, a data interaction module collects building operation data and power grid carbon signals in real time; the dynamic modeling module constructs a building dynamic optimization model with the goal of carbon cost minimization and generates carbon emission prediction; the centralized optimization solving module is used for discretely solving the continuous optimization problem by adopting a pseudo-spectral method to obtain an optimal equipment power sequence; according to the method, the carbon emission is accurately predicted by building a multi-module cooperative control system, combining the steps of parameter identification, rolling window determination and the like, the optimal power control sequence is solved, the power is dynamically adjusted by building a carbon emission cooperative rule, and the optimal power control sequence is obtained. And overall optimized operation of the urban public building group heating and ventilation system is realized.

The invention provides an airport terminal building heating, ventilation and air conditioning load prediction system and method based on a micro-service architecture. The method comprises the following steps: collecting multi-source heterogeneous original data and extracting a key feature set; mining space-time relevance between people flow and equipment operation, and outputting a space-time characteristic matrix; and constructing a multi-algorithm fusion framework, carrying out cross-terminal distributed cooperative training and dynamic weight aggregation through federal learning, introducing a lion group optimization algorithm to optimize global parameters, and outputting an airport terminal heating ventilation air conditioning load prediction result. Fusion data are standardized, key features are extracted in combination with automatic feature engineering, and the data utilization rate and feature relevance are improved; then capturing a space-time association mode by using a space-time diagram convolutional network, constructing a multi-algorithm fusion framework and dynamically allocating weights; then realizing distributed cooperative training by adopting federal learning, and optimizing parameters in combination with a lion group optimization algorithm to enhance the generalization ability of the model; and finally, through incremental learning dynamic iterative optimization, the prediction precision is continuously improved.

The present invention provides a process to implement and control localized air- conditioning of occupied regions in large confined space to reduce HVAC energy consumption comprising delineating the space into multiple micro-zones based on a first level of protocol and static parameters thereof including planned activities, occupancy patterns, diffuser characteristics, and furniture layout and controlling airflow through diffusers in the occupied and unoccupied micro-zones to achieve setpoint temperature or thermal comfort only in the occupied regions using minimum HVAC energy through implementing a second level of protocol with dynamic parameters relating to the micro-zones including real-time occupancy, thermal comfort preferences, ambient conditions, and inter-micro-zonal thermal interactions.

A recreational vehicle includes a towable chassis including wheels, a utility layer on the chassis, a house on the chassis above the utility layer, and one or more functional interconnections. The utility layer includes one or more utilities. The house includes one or more appliances. Each of the one or more functional interconnections couples at least one of the one or more utilities to at least one of the one or more appliances. The functional interconnections may comprise one or more of plumbing, wires, or ducts. Examples of utilities include a battery, an electrical system, a pump, a water heater, or a heating, ventilating and air conditioning (HVAC) unit. Examples of appliances include a faucet, a drain, a toilet, lighting, an electrical outlet, a stove, a cooktop, a refrigerator, or one or more air vents.

The application discloses a kind of two-stage optimization control method and system for user-centered heating ventilation air conditioning system, it is related to building energy consumption control field, including the following steps: step 1, establish second-order equivalent thermal parameter model, obtain the online identification result of model initial parameter by genetic algorithm iteration, obtain the initial prediction value result of indoor temperature;Step 2, establish TimesNet model, construct the input and output feature set of model, obtain the correction value result of indoor temperature;Step 3, establish fusion model and complete accurate prediction to indoor temperature;Step 4, realize group-level regulation and control of indoor temperature based on model predictive control, obtain temperature setting interval;Step 5, realize individualized regulation and control of indoor temperature based on deep Q network, obtain temperature setting value.The application has significant technical advantages in modeling accuracy, control performance and response efficiency.

The utility model belongs to the technical field of heating ventilation air conditioners, and particularly relates to a defrosting device and an air source heat pump device. The defrosting device comprises a hot air pipe, the hot air pipe is arranged around the fin type heat exchanger, and air holes facing one side of the fin type heat exchanger are formed in the hot air pipe. The vortex tube comprises a vortex chamber, an air inlet, a cold air section and a hot air section, the air inlet, the cold air section and the hot air section are communicated with the vortex chamber, the air inlet is used for introducing compressed air, the hot air section is connected with the hot air pipe, and the air outlet direction of the cold air section faces the outer side of the fin heat exchanger. According to the defrosting device, compressed air is used for defrosting, so that the air source heat pump device can stably heat and supply heat for a long time in a low-temperature environment, sufficient compressed air resources of a plant are reasonably utilized to be matched with the defrosting device, non-stop defrosting of a heat pump unit can be achieved, and the heat exchange performance of the heat pump unit is effectively improved.

A heating, ventilation, and air conditioning (HVAC) system for a vehicle having a rechargeable energy storage system includes a refrigerant circuit in which a refrigerant flow circulates. The refrigerant circuit includes a compressor, an internal condenser, and a chiller heat exchanger. A coolant circuit is fluidly connected to the refrigerant circuit and has a coolant flow circulating therein. The coolant circuit includes the chiller heat exchanger, the internal condenser, a heater core, a rechargeable energy storage system (RESS), and a three-way coolant valve to selectively direct the coolant flow through the RESS and / or bypass the RESS along a bypass passage.

The present disclosure provides a heat exchanger (100) for a heating, ventilation, and / or air conditioning (HVAC) system (30), the heat exchanger comprising: a header (106) having a longitudinal axis (160); a first plurality (114) of microchannel tubes (102) coupled to the header (106), wherein each microchannel tube (102) of the first plurality (114) of microchannel tubes (102) has a first width (150); and a second plurality (116) of microchannel tubes (102) coupled to the header (106), wherein each microchannel tube (102) of the second plurality (116) of microchannel tubes (102) has a second width (152) that is greater than the first width (150).

The purpose of the present invention is to suppress performance degradation of a heat exchanger with time in an air-conditioning device provided with the heat exchanger. An HVAC system (1) is provided with: a heat exchanger (3) that exchanges heat between exhaust gas discharged to the outside of a vehicle compartment and outside air introduced into the inside of the vehicle compartment; and a housing (5) in which are formed an exhaust passage (5A) that guides exhaust gas to the exhaust gas introduction port (3A) of the heat exchanger (3), an outside air passage (5B) that guides outside air to the outside air introduction port (3B) of the heat exchanger (3), a return air passage (5C) that bypasses the heat exchanger (3) and circulates return air in the vehicle interior, and a discharge port (5D) that discharges the exhaust gas that has passed through the heat exchanger (3) to the outside of the vehicle interior. Furthermore, the HVAC system (1) is provided with: a filter (7) disposed on the upstream side of the exhaust introduction port (3A) and the outside air introduction port (3B) of the heat exchanger (3) so as to straddle at least the exhaust passage (5A) and the outside air passage (5B) among the exhaust passage (5A), the outside air passage (5B), and the return air passage (5C); and a damper (9) that adjusts the amount of external air introduced into the vehicle interior by changing the aperture ratio of the external air passage (5B) and the air return passage (5C) on the upstream side of the filter (7).

An operator learning model generator is provided for training a smooth operator learning model for predicting airflow dynamics in a room used by a controller connected to a heating, ventilation and air conditioning (HVAC) system. The operator learning model generator includes an interface circuit configured to receive a training dataset via a network connected to a simulation computer, wherein the training dataset includes solution trajectories of airflow in the room for various times series of control actions given to the HVAC system, a memory configured to store the smooth operator learning model comprising an auto-encoder and a neural ordinary differential equation, the training dataset, and training instructions for the smooth operator learning model, and a processor configured to train the smooth operator learning model stored in the memory, wherein the training instructions comprise a jerk regularization that enforces smoothness of the dynamics predicted by the smooth operator learning model.