14235results about "Efficient regulation technologies" patented technology

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

The invention relates to an anti-electromagnetic interference intelligent cooling fan system based on dynamic filter circuit design. The system comprises a dual analysis module used for performing time domain and frequency domain dual analysis on a rotating speed feedback signal of a double-ball fan to obtain a multi-dimensional electromagnetic interference characteristic model, and configuring filter network parameters to obtain a target three-stage cascade dynamic filter network; the signal purification module is used for collecting first fan operation parameters and inputting the first fan operation parameters into a target three-stage cascade dynamic filter network for signal purification to obtain filtered Hall sensor signals and filtered power supply signals; the closed-loop control calculation module is used for performing closed-loop control calculation to obtain a non-linear PWM duty ratio control quantity and a phase distribution parameter; and the phase difference control module is used for carrying out phase difference control to obtain a multi-fan cooperative control strategy. According to the invention, high-precision control and anti-interference capability in a wide rotating speed range are realized, and the problem of mutual influence of electromagnetic interference when multiple fans are connected in parallel is solved.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention belongs to the technical field of motor control, and discloses a control method of a motor for an inverter compressor, which comprises the following steps: embedding a distributed thermocouple array in a motor stator slot to acquire temperature data of the motor, synchronously acquiring operation parameters of the motor, generating a dynamic thermal field characteristic spectrum through a data fusion algorithm, coordinates of the hot spot areas with the temperature abnormally increased are marked, a dynamic correlation model is established, and heat conduction characteristic parameters in the dynamic correlation model are updated through a sliding window algorithm; outputting a predicted temperature rise curve through the dynamic correlation model, adjusting a PWM control strategy of the inverter, inputting the PWM control strategy to a motor driving unit to execute preliminary regulation and control, collecting feedback data to generate a multi-dimensional feedback feature vector, iteratively optimizing a combined weight of a switch interval compensation amount and a harmonic injection strategy through a gradient descent algorithm, and performing harmonic injection control. And a global optimal strategy considering the heat dissipation efficiency and the operation stability is formed, and the operation stability and the energy efficiency level of the inverter compressor under various working conditions are remarkably improved.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention provides an air conditioner personalized intelligent control method, system and device and a storage medium, and relates to the technical field of air conditioner control, the method comprises the following steps: combining first people flow data and historical people flow data of a target area within a first preset duration to predict second people flow data within a target duration; determining the reference temperature of the target area within the target duration in combination with the outdoor temperature and the area characteristics of the target area; in combination with the reference temperature and the second visitor flow rate data, temperature adjusting parameters of the target area within the target duration are determined, and an initial operation scheme of an air conditioner of the target area is formulated according to the temperature adjusting parameters; and according to the environment monitoring parameters of the target area, determining an environment load coefficient, and according to the environment load coefficient, generating a target operation scheme. The method has the technical effects that the environment change is predicted in advance, the air conditioner operation parameters are adjusted in time, and it is guaranteed that the indoor environment can reach the comfortable state in time.

The invention relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention relates to a multi-dimensional air purification method and system based on AI intelligent big data deep analysis, and solves the problem that it is difficult for an air purifier to achieve uniform and efficient purification of the whole indoor space. According to the coordinates and the areas of the sub-regions contained in the target region cluster, the mass center position of the target region cluster is obtained through calculation and serves as the target position of the air purifier; a path planning algorithm is used for planning a moving path from the current position to the target position for the air purifier, and the air purifier is controlled to move according to the moving path; and the size, the pollutant concentration and the type of the polluted area of the area cluster are obtained and input into a preset air purifier purification parameter adjustment model, air purifier purification parameters are output, and purification parameter adjustment is conducted according to the air purifier purification parameters after the air purifier moves to the target position. The indoor air purification device has the following effect that the indoor air purification efficiency and quality are improved.

The invention provides a subway station central air conditioner full-link energy-saving system and method based on wind balance. Belongs to the technical field of air-conditioning energy conservation. The method comprises the steps that environmental data inside and outside a subway station are collected in real time through a sensor network, and the collected environmental data are uploaded to a central air conditioner full-link energy-saving control platform; the central air conditioner full-link energy-saving control platform dynamically adjusts the air volume of the air supply pipe and the air return pipe through fan control according to the collected data; by collecting environment data in real time and combining an advanced control algorithm, the system can dynamically adjust the rotating speed of a fan, the load of a refrigerating unit, the ratio of fresh air to return air and the like, so that the running state of the air conditioning system is optimized, energy consumption is effectively reduced, and the overall energy-saving effect of the system is improved.

The invention relates to the field of machine room water pump control methods, in particular to an efficient machine room water pump frequency conversion control system and method. Modules of intelligent prediction and optimization, dynamic pressure and flow control, multi-sensor fusion, fault prediction and adaptive control, energy management and optimization and the like are integrated; and prospective and self-adaptive operation of the cooling system is realized. The system not only can predict the cooling demand according to historical data and real-time environmental parameters and adjust the working state in advance, but also can monitor and accurately control the pressure and flow of the cooling water system in real time and ensure the cooling efficiency. Through multi-sensor fusion, the system comprehensively monitors the operation state, and in combination with fault prediction and adaptive control, potential faults can be prevented, and the system stability is guaranteed. The energy management and optimization module intelligently dispatches operation of a cooling water tower fan and a cooling water pump, efficient utilization of energy is achieved, and the energy efficiency and reliability of the cooling system are remarkably improved on the whole.

The invention discloses an air purification system for a clean workshop. The air purification system comprises a self-adaptive control unit, an air quality monitoring module, an air flow control assembly, an environmental parameter adjusting assembly, a self-optimization filtering assembly, a human-computer interaction interface and predictive maintenance system, an energy efficiency optimization module and a remote diagnosis and early warning system. On the basis of intelligent technologies such as machine learning, reinforcement learning and a data fusion algorithm, real-time monitoring, dynamic optimization and energy efficiency management of the air quality of the clean workshop are achieved. Based on real-time data and historical data, an algorithm is used for carrying out self-adaptive optimization on the air purification system, and manual intervention is reduced. And the running state of the system is dynamically adjusted through the energy efficiency optimization module, so that the energy consumption is reduced while the cleanliness is ensured. The system can diagnose equipment faults in advance, reduce downtime and prolong the service life of equipment. And due to a man-machine interaction interface and a remote monitoring function, an operator can know the state of the system at any time and carry out regulation and control.

The invention discloses an air conditioning system and a control method thereof. The air conditioning system comprises an air conditioning outdoor unit; the air conditioning outdoor unit comprises a compressor, an outdoor heat exchanger and an outdoor fan; the outdoor fan is at least provided with a first working mode and a second working mode; and when the outdoor fan runs in the first working mode, a wind gear is higher than that when the outdoor fan runs in the second working mode. The control method comprises the following steps that S1, the air conditioning system is controlled to enter arefrigeration mode; S2, according to the outdoor environment temperature and / or preset hours, the outdoor fan is controlled to enter the first working mode or the second working mode. According to the control method of the air conditioning system, the air conditioning system can control the outdoor fan to enter the first working mode or the second working mode according to the outdoor environmenttemperature and / or the preset hours, and when the outdoor fan runs in the second working mode, the wind gear of the outdoor fan is lowered, so that the use reliability the air conditioning system cannot be affected, and the requirement of a user on comfortableness can be met.

The invention provides an energy-saving control method based on dynamic air conditioning operation data, and relates to the technical field of intelligent control and energy-saving optimization of an air conditioning system.The energy-saving control method comprises the steps that operation data, including temperature, humidity, air velocity and energy consumption data, of an air conditioning system are collected, and a comfort index, an air quality index and an energy efficiency index are generated; the method comprises the following steps of: generating an air conditioning index comprehensively, setting a control target parameter of a system, further generating an operation standard value, dynamically generating a control strategy which comprises the steps of adjusting the switching time of a cooling mode and a heating mode of a fan, optimizing the operation speed and time of the fan and adjusting the operation frequency of a compressor, and finally, if the generated air conditioning index exceeds the standard value, controlling the system to operate. And if not, control strategy adjustment is repeated until the air conditioning index is within the standard value. The control strategy is dynamically adjusted by collecting and analyzing the operation data of the air conditioning system, efficient operation of the system is achieved, and the comfort level of a user and the energy-saving effect are improved.

The invention relates to the technical field of air purification, in particular to an intelligent control system and method based on air purification. The method comprises the following steps: acquiring particulate matter data in an area; performing particulate matter concentration feature extraction on the particulate matter data in the region to obtain particulate matter concentration feature data in the region; performing particulate matter concentration correction on the particulate matter concentration data based on an environment sensor to generate particulate matter concentration correction data; performing heat map conversion on the particulate matter concentration correction data to generate a particulate matter concentration time sequence change heat map; performing particulate matter community area division on the particulate matter concentration time sequence change heat map to generate particulate matter cluster area data; and performing cluster region concentration prediction on the particulate matter cluster region data to generate particulate matter cluster concentration prediction data. By introducing technologies such as particulate matter concentration correction, time sequence change heat map analysis, multi-stage filtration and intelligent control, the control accuracy and flexibility of the air purification equipment are improved.

The invention relates to the technical field of mine ventilation control, in particular to a mine fan ventilation control method and system based on digital twinning. The method comprises the following steps: carrying out data preprocessing on acquired mine monitoring data; performing feature extraction on the preprocessed mine monitoring data; utilizing a random forest algorithm to predict the relationship between the data characteristics and the fan air volume; a digital twinborn model is used for resolving the relation between the data characteristics and the fan air volume, and a fan ventilation control strategy is obtained; and generating a PLC control instruction based on the fan ventilation control strategy. And through multi-source data fusion and deep analysis, the real-time ventilation requirement of the mine can be accurately grasped. Based on random forest algorithm prediction and digital twinborn model calculation, dynamic and accurate adjustment of the air volume of the draught fan is achieved, it is ensured that the concentration of harmful gas in all underground areas is maintained within a safety threshold value, the oxygen content is sufficient, an ideal environment is created for personnel operation and equipment operation, and the safety and reliability of a ventilation system are remarkably improved.

The invention provides a range hood control method and system based on the Internet of Things, and relates to the technical field of artificial intelligence and the Internet of Things, and the method comprises the steps: obtaining multi-source layered environment characteristic data of heating power, oil smoke and airflow through constructing a layered data collection network, and carrying out the processing of edge calculation and a cloud server, drawing force required by each area is calculated, a self-adaptive fan control curve is generated, and then an initial control parameter set containing a main fan rotating speed value, an auxiliary fan rotating speed value and an air deflector angle value is generated; the method comprises the following steps: constructing a vortex flow prediction model based on a vortex tensor and a velocity divergence by using a cloud edge collaborative architecture, predicting an airflow movement track, calculating a real-time airflow deviation degree, dynamically adjusting an initial control parameter group to generate a target control parameter group, and finally continuously optimizing the prediction model through a deep reinforcement learning network.

A remote test apparatus is configured to simulate an environment of at least one remotely located room and configured to receive at least one air sample from the at least one remotely located room. The apparatus includes a sealed enclosure forming an interior cavity and including an inlet in connection with the at least one remotely located room and an outlet in connection with a sample collection unit. The sample collection unit is configured to communicate the at least one air sample from the remotely located room at a first flow rate. The apparatus further includes at least one sensor disposed in the interior cavity and at least one air transfer unit configured to transfer test air from the interior cavity of the sealed enclosure to the at least one sensor at a second flow rate.

The invention provides an air guide assembly and air treatment equipment, and relates to the technical field of air treatment equipment. The air guide assembly is mounted at an air outlet of the air treatment equipment and comprises an adjusting assembly and a plurality of air guide blades, and the adjusting assembly comprises a bearing plate and a plurality of air guide blades movably connected to the bearing plate; the bearing plate extends in the length direction of the air outlet, and the air guide blades are sequentially arranged along the plate face of the bearing plate. The driving assembly drives the bearing plate to change in position relative to the air outlet, and the driving assembly drives the air guide blades to change in position relative to the bearing plate. According to the air guide assembly, the air supply angle of the air treatment equipment can be flexibly adjusted, and the air supply coverage area of the air treatment equipment is enlarged. In addition, the driving mode of the air guide assembly is simpler, and space saving and energy consumption reduction are facilitated.

Techniques for operating an environmental sensing system are described. In an example, a cloud-based server system receives a first indication that a first type of air pollutant is present within a first structure from a first plurality of indoor air quality (IAQ) sensing devices positioned within the first structure. A second structure within a predefined distance to the first structure is then identified by the server system. The server system then determines that the first type of air pollutant is not present within the second structure. The server system then causes a second plurality of IAQ sensing devices positioned within the second structure to change an operating mode from a normal sensitivity mode to a high-sensitivity mode.

The invention relates to the technical field of clean environment control, in particular to a dynamic pipe network and model predictive control clean room energy-saving pressure control method and system. Obtaining current pipe network impedance according to the pipe network impedance curve; calculating the current valve impedance according to the valve opening; calculating the total pressure drop of the current pipe network; constructing a fan dynamic model based on a fan similarity law to calculate the air volume at the next moment; a clean room pressure difference dynamic model is constructed, and the clean room pressure difference at the next moment is calculated; constructing a state space model and a target function of a model prediction controller; and when the pressure difference of the clean room is unstable, predicting the change trend of the pressure difference of the clean room in the next time period, and reversely solving the optimal fan frequency and valve opening of the discretization state space model by using the model prediction controller so as to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The control precision and robustness of the pressure difference of the clean room are remarkably improved, and meanwhile energy consumption is reduced.

The invention discloses an AI-based intelligent energy management method and system for air conditioning equipment. The method comprises the following steps that indoor environment data are collected in real time through a sensor on the air conditioning equipment; cleaning, de-noising and normalizing the acquired environment data; collecting air conditioner setting preference data of the user; based on the collected data, reasoning and result output are conducted on operation of the air conditioner through an AI model in the edge computing device; according to the result output, an air conditioner control strategy adapting to the current environment and user requirements is generated; dynamically adjusting the operation mode of the air conditioner based on the real-time load and the power grid condition; the air conditioner control strategy is adjusted according to feedback data in the air conditioner operation process; according to the AI-based intelligent energy management method and system for the air conditioning equipment, a comfortable environment can be provided, efficient energy management can be achieved, the overall energy consumption is reduced, and the pressure on a power grid is reduced.

An IAQ sensor module includes: a sensor configured to measure an amount of an item in air, the item being one of particulate matter, volatile organic compounds, and carbon dioxide; a minimum module configured to selectively store the amount of the item as a minimum value of the amount when a mitigation device has been on for at least a predetermined period, the mitigation device being configured to decrease the amount of the item in the air when on; a storing module configured to selectively store the minimum value as an initial minimum value; an offset module configured to determine a drift offset for the sensor based on a difference between the minimum value and the initial minimum value; and an adjustment module configured to determine an adjusted amount of the item in the air at the IAQ sensor module based on the amount and the drift offset.

The invention discloses a laboratory air conditioning system and a control method thereof, and particularly relates to the technical field of air conditioning. Intelligent monitoring and dynamic adjustment of air quality in an experiment area are achieved through coordinated work of the central control module, the zone control module and the zone sensor module, the system evaluates the air pollution severity degree of each area in real time according to air quality sensing data, and through dynamic linkage of the air purification module, the air flow control module and the temperature and humidity adjusting module, the air quality of the experiment area can be monitored and adjusted dynamically. In addition, by predicting future air quality changes and adjusting air quality parameters and optimizing energy consumption in advance according to prediction results, it is ensured that energy consumption is reduced while the air quality meets experimental requirements, and the precision and efficiency of laboratory air quality management are remarkably improved.

The invention discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention discloses an airport terminal fresh air energy-saving optimization control method based on passenger space-time distribution prediction, and relates to the technical field of intelligent energy saving, and the method comprises the following steps: S100, collecting the real-time position and moving speed of a passenger, constructing a passenger space-time distribution change model, and forming a prediction basis of air pressure dynamic adjustment; and S200, based on the passenger space-time distribution change model, calculating air pressure gradient distribution of each functional area of the terminal, identifying local positive pressure and adjacent negative pressure areas, establishing a micro pressure difference grading early warning index, and determining a ventilation adjustment demand. By dynamically predicting passenger distribution, adjusting air supply and static pressure in real time and combining pressure difference early warning, airflow monitoring and countercurrent sealing control, airflow order is ensured, pollution gas is prevented from flowing backwards, and air quality and comfort are guaranteed. Based on a control model and an energy-saving strategy library of full-process data training, intelligent self-adaptive adjustment of the fresh air system is achieved, ventilation safety and energy efficiency are improved, and terminal service experience and operation efficiency are optimized.

The invention relates to the technical field of air-cooled air conditioner control, in particular to an air-cooled air conditioner control method and system based on deep learning, and the method comprises the following steps: obtaining sensor readings of indoor temperature, outdoor temperature and humidity and Wi-Fi channel state information, judging the position number and the activity state of personnel, calculating the position distribution of the personnel in combination with a time window, and calculating the position distribution of the personnel. And integrating to obtain a multi-dimensional environment and load state vector. According to the method, indoor and outdoor temperature, humidity, Wi-Fi channel state information and personnel position distribution and activity states are integrated through a multi-dimensional environment and load state vector, the personnel density change trend is dynamically calculated through a time window, the comprehensiveness and real-time performance of environment perception are enhanced, and high-dimensional data support is provided for control decision making. And a basic comfort degree deviation index is constructed based on the temperature deviation value and the humidity deviation value, and a multi-target performance index set is generated in combination with the wind speed value, the compressor start-stop frequency and the energy consumption power.

The invention relates to an air conditioner dynamic optimization method and system based on deep learning, and belongs to the technical field of air conditioner intelligent control. The method comprises the steps of obtaining air conditioner operation data, constructing an air conditioner operation data set through air conditioner data preprocessing according to the air conditioner operation data, and obtaining an air conditioner user preference model through multi-layer perceptron training and testing according to the air conditioner operation data set, according to the air conditioner user preference model, an air conditioner user preference DQN model is constructed through an initialized DQN; environment sensor data are collected in real time, a user air conditioner optimal control strategy is output through the air conditioner user preference DQN model according to the environment sensor data, and air conditioner equipment is regulated and controlled in real time through an air conditioner control system according to the user air conditioner optimal control strategy. Therefore, the air conditioner parameters are dynamically optimized based on the user comfort level and the energy-saving target.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

The invention relates to the technical field of air conditioners, in particular to a machine room air conditioner operation state control method and system.The method comprises the steps that parameter data at the current moment are input into a temperature prediction model, and predicted temperature data at the next moment are obtained; wherein the parameter data comprises an actual temperature and a target temperature in the machine room; acquiring a current predicted difference value between the predicted temperature data and the target temperature; when the current prediction difference value is greater than or equal to a threshold value, evaluating a temperature prediction model to obtain an evaluation result; the evaluation result is the similarity between the historical actual temperature data and the corresponding historical predicted temperature data; when the evaluation result is larger than a set value, the current machine room air conditioner operation state is controlled and adjusted; otherwise, repeated training is carried out according to the comprehensive loss function to update the temperature prediction model. According to the scheme, the accuracy of machine room air conditioner operation state control can be improved.

A computer-implemented method of controlling ventilation in a closed space (240, 310) and related system. The method comprises receiving. at a controller (275) comprising a processor (560)), from at least one sensor consisting of a temperature sensor (360, 370), input relating to temperature from the temperature sensor (360, 370) (step 100): calculating. using a processor (560), the desired speed of at least one extractor fan (270) based upon the input from the temperature sensor (step 110): transmitting from the controller to the extractor fan (270) a signal to power the at least one extractor fan (270) to the desired speed (step 120); calculating. using the processor (560), the desired intake of air from at least one air intake (285) based upon the speed of the at least one extractor fan (step 130); transmitting from the controller (275) to the air intake (285) a signal to operate the at least one air intake for the desired intake of air (step 140)).