2230 results about "HVAC" patented technology

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention discloses a central air conditioner load self-adaptive regulation and control method based on artificial intelligence, which relates to the technical field of heating ventilation air conditioner automatic regulation and control, and comprises the steps of edge end data preprocessing, deep learning load prediction and self-adaptive updating, intelligent control strategy, closed-loop feedback and continuous optimization and fault diagnosis and remote operation and maintenance. Through edge end denoising and feature labeling, the data transmission pressure is remarkably reduced, and the response speed is increased; the deep learning model is combined with online iteration and reinforcement learning, key parameters such as chilled water temperature and water pump frequency are accurately regulated and controlled, and energy consumption and comfort are considered; according to the method, prediction errors and equipment states are monitored, a model and a control strategy are synchronously updated when abnormity is detected, fault early warning is triggered in combination with health assessment, energy consumption is effectively reduced, the service life of equipment is prolonged, and finally an efficient and stable self-adaptive regulation and control system capable of being operated and maintained online in a long period is constructed.

A system and method for HVAC optimization is disclosed. The system may include a digital twin (112) of an HVAC system (114) installed in a premises (108). The digital twin (112) is based on design parameters, operational data, and PMV-based thermal comfort analysis. The system further includes an RL agent (202) that is configured to receive and process at least real-time environmental conditions, occupancy patterns, and PMV value associated with the premises (108) of the HVAC system (114), identifying one or more optimal actions to adjust one or more HVAC parameters. The digital twin (112), driven by the identified optimal actions, may dynamically simulate the HVAC system (114) and the RL agent (202) may optimize operations of the HVAC system (114) based on the identified optimal actions, ensuring seamless and adaptive thermal comfort and energy efficiency management within the premises (108).

The invention discloses a green building intelligent lighting and energy collaborative optimization system based on multi-source data fusion. According to the system, firstly, multi-source information such as indoor and outdoor illumination, weather, personnel occupation and electricity price is subjected to weighted fusion through dynamic confidence, and an accurate real-time environment state is constructed; then, model predictive control is combined with a multi-objective optimization strategy, and a sunshade device, intelligent lighting and a heating ventilation air conditioner are cooperatively adjusted, so that the comprehensive energy consumption or operation cost is minimized on the premise that the indoor illumination and temperature comfort requirements are met; besides, the self-adaptive module continuously learns and adjusts the thermal characteristics and the use mode of the building through online model parameter correction and occupancy probability prediction, and it is ensured that the energy-saving effect is stable for a long time; the system drives each execution device through a standard building self-control interface, is easy to integrate in a newly built or reformed project, and can significantly improve the energy utilization efficiency and the indoor environment quality.

A heat pump system for a vehicle may efficiently adjust the temperature of heating elements by using one 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 one heat-exchanger.

A heat pump system for a vehicle may include: a compressor, a heating, ventilation, and air conditioning (HVAC) module, a heat-exchanger, a first expansion valve, a connection line, and a chiller. The heat pump system may improve heating performance for heating a vehicle interior by employing a gas injection device selectively operating at the time of heating a vehicle interior to increase the flow amount of refrigerant.

The invention relates to the technical field of energy consumption management, and particularly discloses an AI-based clean room dynamic energy consumption management system and method. According to the method, a deep learning algorithm is introduced to carry out deep interaction analysis on clean room indoor air quality parameter time sequence data and environment change driving event data such as the number of indoor personnel, the external environment temperature, the room door opening and closing state and the equipment power, so that intelligent reasoning of the dynamic evolution process of the clean room indoor air quality is realized; according to the method, the air quality parameter prediction result is compared with the preset air quality standard range so as to predict the air quality change in the future short time under the current driving event, and then the short-time air quality parameter prediction result is compared with the preset air quality standard range so as to carry out feedforward adjustment on the HVAC system operation parameters of the clean room according to the difference between the short-time air quality parameter prediction result and the preset air quality standard range. According to the method, environmental changes can be actively predicted, smooth pre-adjustment is conducted in advance, the air quality in the clean room is kept within the preset standard range, meanwhile, unnecessary energy waste is remarkably reduced, and the environmental stability is improved.

The embodiment of the invention discloses a heating and ventilation control method and device based on reinforcement learning and a medium, and relates to the technical field of heating and ventilation control, and the method comprises the steps: determining predicted temperature data corresponding to a target building through a pre-constructed Mamba time sequence prediction model, and carrying out the reinforcement learning pre-training through the predicted temperature data and a preset space-time constraint matrix, determining an initial heating and ventilation control strategy; performing initial regulation and control on a heating ventilation air conditioning system in the target building according to the initial heating ventilation control strategy, and collecting real-time building data corresponding to the target building; according to real-time building data, performing dynamic partitioning on a target building, determining a plurality of dynamic control partitions, constructing partition state space description based on the dynamic control partitions and the space-time constraint matrix, and performing targeted online reinforcement learning training through the partition state space description by using a progressive exploration strategy, so as to improve the training efficiency. And generating a heating and ventilation control strategy corresponding to the target building so as to control the target building through the heating and ventilation control strategy.

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.

A method for automatic directional control of an air conditioning vent including detecting a vehicle location and a vehicle orientation, determining a sun position with respect to the vehicle location and the vehicle orientation, determining a solar heat gain location within a vehicle cabin in response to the sun position, generating a control signal indicative of the solar heat gain location, and positioning the air condition vent to direct an airflow towards the solar heat gain location and to adjust the flow rate of the airflow in response to the control signal.

In order to efficiently perform heating of a vehicle interior in a state in which the external temperature is low and heat generated by a heating element is not sufficient in an early stage of driving the vehicle, a heat pump system for a vehicle is provided. The heat pump system utilizes various heat sources in a general heating mode of the vehicle interior, and at the same time, maximizes the system performance by increasing flow of the refrigerant. The heat pump system may include: a compressor, a heating, ventilating, and air conditioning (HVAC) module; a heat-exchanger; a first expansion valve; a first connection line; a chiller; a second expansion valve; a second connection line; a third connection line; and a fourth connection line.

A system (100) for HVAC optimization is disclosed. The system (100) may include a digital twin (112) of an HVAC system (114) installed in a premises (108). The digital twin (112) is based on design parameters, operational data, and PMV-based thermal comfort analysis. The system (100) further includes an RL agent (202) that is configured to receive and process at least real-time environmental conditions, occupancy patterns, and PMV value associated with the premises (108) of the HVAC system (114), identifying one or more optimal actions to adjust one or more HVAC parameters. The digital twin (112), driven by the identified optimal actions, may dynamically simulate the HVAC system (114) and the RL agent (202) may optimize operations of the HVAC system (114) based on the identified optimal actions, ensuring seamless and adaptive thermal comfort and energy efficiency management within the premises (108).

The invention discloses a roof radiant cooling and independent fresh air dehumidification system mixed operation comprehensive control method, and belongs to the technical field of building environment control and heating ventilation air conditioning. The method comprises the steps that the radiant cooling capacity and the dehumidification capacity of a surface air cooler are calculated through a heat balance equation, and an optimization objective that a top plate radiation system bears 70% sensible heat load and a fresh air system bears 90% humidity load is set; an anti-condensation strategy is triggered by combining dew point temperature monitoring, a control variable is adjusted by taking minimization of energy consumption as a target in a rolling time domain, and it is ensured that the indoor temperature ranges from 23 DEG C to 26 DEG C, the humidity ranges from 40% to 60%, the black ball temperature difference is smaller than or equal to 2 DEG C, and the condensation risk is smaller than or equal to 6%. The method is suitable for energy-saving temperature and humidity regulation and control of large public buildings, and efficient cooperative processing of sensible heat and latent heat loads is achieved.

A method and system for controlling a heating, ventilation, and air conditioning system (HVAC) in a building based on adaptive model predictive control (MPC) is disclosed, and belongs to the technical field of intelligent building adaptive control. The method may include: acquiring operation data of the HVAC under different working conditions, and performing preprocessing on the acquired data; calculating a marginal fitness measure (MAD) of each data point for each class based on the preprocessed data, and calculating a fitness measure of each data point for each class by combining MADs of all descriptors to obtain a global fitness measure (GAD); setting a GAD threshold, and classifying current operation states of the system according to classification rules; and adopting adaptive control strategies for different operation states since model parameters of each class are fixed after classification.

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 provides a multifunctional heating ventilation air conditioner waste heat utilization method and system, and the method comprises the following steps: 1, obtaining multi-source data of an air conditioner system and an external heat source, carrying out the preprocessing, carrying out the grading collection and preliminary integration of the multi-source waste heat, and dividing the multi-source waste heat into high-temperature waste heat, medium-temperature waste heat and low-temperature waste heat; multi-source data of an air conditioning system and an external heat source are collected, condensation heat of a compressor, industrial waste heat and other multiple types of waste heat are integrated and subjected to staged treatment, and a high-temperature power generation-medium-temperature heat supply-low-temperature heat storage gradient utilization system is constructed; an LSTM neural network thermal load prediction model and an intelligent algorithm are combined to dynamically adjust a waste heat distribution path so as to realize self-adaptive switching of a refrigeration mode, a heating mode and a hot water mode, and meanwhile, a cascade system, photovoltaic energy, geothermal energy and the like are coupled through a direct-current micro-grid to construct a hybrid energy supply system. The problems that in the prior art, waste heat sources are single, energy efficiency is low due to the fact that gradient utilization is lacked, dynamic regulation and control are insufficient, and cooperation with renewable energy sources is not achieved are sequentially solved.

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.

The invention discloses a control method and device of an HVAC system, a clean room HVAC system, a medium and equipment. The method comprises the steps that indoor and outdoor environment parameter values of a clean room and HVAC equipment operation state data are collected in real time; inputting the collected environmental parameter values into an operation parameter prediction model to obtain control information of the HVAC system; and adjusting operation parameters of the HVAC system based on the control information of the HVAC system and the operation state data of the HVAC equipment. By adopting the scheme, the response speed of the HVAC system can be improved, and the energy consumption of the HVAC system can be reduced.

The invention discloses an HVAC intelligent regulation and control method and system based on personnel dynamic prediction, and the method comprises the steps: deploying a plurality of monitoring nodes in a building regulation and control region through a sensing layer, collecting the number of personnel and environment parameter data in the region in real time, and transmitting the data to a comprehensive regulation and control module according to a standard format; predicting the number of people in a short-term dimension and a historical dimension, and outputting a comprehensive predicted number of people after weight fusion; a decision-making layer calculates a target temperature set value according to the real-time number of people and the comprehensive predicted number of people in combination with the thermal comfort temperature and the energy-saving temperature, and generates a regional HVAC equipment regulation and control instruction based on a PID control algorithm; the execution layer adjusts HVAC equipment operation parameters of the corresponding area according to the regulation and control instruction, and the total load of a building air conditioner is synchronously calculated so as to adjust the power of an air conditioner host; and analyzing historical operation data to optimize regulation and control parameters, detecting abnormal data, generating a fault diagnosis report, and realizing iterative optimization.

The invention discloses a method for predicting dynamic distribution of an indoor air flow field and a temperature field of a building. The method comprises the steps of performing grid division on a target space to obtain three-dimensional grid units, setting boundary attributes of the target space and initial values of air field parameters, and then obtaining vector velocities of the three-dimensional grid units according to an air transfer velocity between two three-dimensional grid units, then, according to the pressure intensity, the temperature and the air transfer speed of the three-dimensional grid units at the current moment, the pressure intensity, the temperature and the air transfer speed of the three-dimensional grid units at the next moment are obtained, and finally, the air flowing moment of the target space is continuously updated and iterated. And the distribution result of the air flow field and the temperature field in the target space in the air flowing process is obtained. The method is beneficial to improving the directivity of heating and ventilation design or air conditioning system adjustment, so that the efficiency of indoor environment quality optimization is improved, and effective assistance is provided for improving the comfort of a user and reducing energy consumption.

A monitoring system having a plurality of sensors configured to collect sensor data associated with a heating, ventilation and air conditioning (HVAC) system, an edge processing unit proximate to the HVAC system and in communication with the plurality of sensors; wherein the plurality of sensors are configured to transmit the sensor data to the edge processing unit, and wherein the edge processing unit is configured to use trained artificial intelligence algorithms to predict the likelihood of failures of one or more components in the HVAC system based on the sensor data.

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.

The embodiments provide a system and method for filtering out smoke and / or toxic gases / fumes from air in a building. The system integrates various smart devices with an HVAC system along with a controlling software application that can change the state of the smart devices, thereby controlling the types and quantities of air moving through the HVAC system at any time. In particular, the system can adjust the openings of one or more smart registers to control how much air passes in and out of the HVAC system over a given period of time. The system can also adjust the opening of a smart intake vent to control how much fresh (outside) air passes into the system over a given period of time. The system can also adjust the opening of a smart furnace valve that is disposed within the furnace.

The invention discloses a fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the steps that operation characteristic data of the heating and ventilation equipment are acquired, and the operation characteristic data comprise normal time period data and fault time period data; determining that a difference value sequence between the normal time period data and the fault time period data does not deviate from fault feature distribution corresponding to a fault type set, and obtaining a target fault type corresponding to the fault time period data from the fault type set based on a fault diagnosis model; determining deviation fault feature distribution of the difference value sequence, and feeding back the difference value sequence to a preset expert terminal; and based on a diagnosis suggestion fed back by the preset expert terminal for the difference value sequence, determining a diagnosis fault type corresponding to the fault period data, and based on the diagnosis fault type, updating the fault type set. According to the embodiment of the invention, the technical problems that the fault diagnosis result of the heating and ventilation equipment is inaccurate and the fault diagnosis generalization capability is poor can be solved.

The invention discloses a micro-grid scheduling optimization method and system, a terminal and a storage medium. A user model of each user node in a micro-grid is established in advance; the user model comprises a plurality of energy models: a heating ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-power grid energy transaction model and an inter-user energy transaction model; determining a balance relation between energy loss and output according to the user model, and determining constraint conditions; establishing a user cost function of each user node according to the cost function of each energy model; and establishing an optimization problem according to the user cost function of each user node, obtaining an optimal solution according to the constraint condition and the optimization problem through an alternating direction multiplier algorithm, and determining an optimal scheduling scheme of the microgrid. All users are designed as peer-to-peer nodes, and a distributed scheduling algorithm is adopted, so that the autonomy of the system can be enhanced, the communication burden can be reduced, and meanwhile, the efficient privacy protection level can be improved.

A method for controlling HVAC equipment includes using a dynamic thermal model that describes heat transfer characteristics of a building to predict a temperature of the building as a function of control inputs to HVAC equipment that operate to affect the temperature of the building. The method includes obtaining time-varying utility prices and a predicted heating or cooling load for the building and performing a predictive control process using the dynamic thermal model, the time-varying utility prices, and the predicted heating or cooling load to generate the control inputs to the HVAC equipment subject to constraints on the temperature of the building.

A vehicle includes a system configured to supply air to a cabin of a vehicle at a target discharge temperature. One or more solar sensors configured to sense a solar radiation on the cabin and one or more temperature sensors configured to sense air within the cabin. A controller is configured to receive a user set temperature, obtain a solar heat load from one or more outputs of the one or more solar sensors, and obtain a feedback temperature from one or more outputs of the one or more temperature sensors. The controller is further configured to determine a radiation temperature corresponding to radiative heat transfer into the cabin, the radiation temperature being a function of the feedback temperature and the solar heat load. The controller may then set the target discharge temperature according to the user set temperature, the feedback temperature, and the radiation temperature.

A heat pump system for a vehicle may effectively adjust a temperature of a heating element by using a cooler in which refrigerant and coolant are heat exchanged. Meanwhile, by using a rear heating, ventilation and air conditioning (HVAC) module equipped with a heat exchanger, the heat pump system can effectively perform cooling and heating of the rear seat.

A method operating an HVAC system is provided. The method includes receiving a concentration of refrigerant. The method includes operating the HVAC system in a leak diagnostic mode if the concentration exceeds a gas concentration threshold. The method includes turning on a compressor to compress the refrigerant. The method includes receiving a first saturated liquid temperature and a first subcooled liquid temperature, and determining a first subcooled value based on the difference. The method includes turning off the compressor for a duration, and turning on the compressor after the duration has elapsed. The method includes receiving a second saturated liquid temperature and a second subcooled liquid temperature, and determining a second subcooled value based on the difference. The method includes determining that the first refrigerant circuit includes a refrigerant leak if the second subcooled value or the first subcooled value exceeds a subcooled threshold value.