853 results about "Chilled water" patented technology

The invention relates to the technical field of air conditioner chilled water control, in particular to an air conditioner chilled water control method and system based on deep learning, and the method comprises the following steps: collecting states of an air conditioner water chilling unit, including water pump frequency and valve opening feedback, and pipe network water supply temperature, return water temperature, flow and differential pressure operation readings; according to the method, the state parameters of the water chilling unit are dynamically collected, the system operation state feature set is constructed, real-time fusion and standardization processing of multi-source heterogeneous data are achieved, the problem of time sequence dislocation caused by sensor sampling frequency differences is solved, the heat load change key input quantity is recognized based on the feature set, and the system operation state feature set is established. And the performance attenuation trend of the water pump is accurately pre-judged in combination with prediction calculation, and extra energy consumption caused by lagging adjustment is reduced. A layered optimization framework with total energy consumption minimization as a target is introduced, water supply temperature, pressure difference setting and unit operation combination are decoupled into independent optimization sub-problems, and invalid work of a water pump is reduced on the premise that the tail end heat requirement is met.

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

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.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention relates to the technical field of industrial control, and discloses an energy-saving air conditioner intelligent control system which comprises an edge computing node, a dynamic load prediction module, a self-adaptive control module, an energy consumption optimization evaluation module and a fault diagnosis module. A data acquisition module, a real-time control module and a communication interface are integrated; based on the historical environment data and the air conditioner operation log, a long-short-term memory network model is adopted to predict the expected load demand in 2-4 hours in the future; dynamically adjusting the operation number of chilled water units, the frequency conversion frequency of water pumps and the opening degree of fresh air valves according to the expected load requirement; the unit cold energy consumption of a single air conditioner is calculated in real time, and a global optimization instruction is generated in combination with a time-of-use electricity price strategy; motor current harmonic characteristics are collected through the edge calculation nodes, the abnormity of the air conditioner is recognized, graded response is conducted, the intelligent control efficiency of the energy-saving air conditioner is improved, and the overall energy consumption cost of the air conditioner is reduced.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention discloses an energy-saving economic dispatching method for a data center energy system based on a prediction model. The method comprises the following steps: establishing a digital twinborn model of the data center energy system; dividing the data center into a plurality of energy consumption partitions, and installing a temperature acquisition device in each energy consumption partition; after a cooling capacity demand prediction model of the data center under each time period partition is established, target combination parameters and target energy efficiency indexes PUE of the chilled water temperature and the air supply temperature of a refrigeration subsystem under each time period are obtained through calculation; establishing a data center energy efficiency index PUE prediction model, and establishing an upper-layer refrigeration subsystem optimization scheduling model by taking the minimum deviation between an energy efficiency index PUE prediction value and a target energy efficiency index PUE, the minimum deviation between actual combined parameters of chilled water temperature and air supply temperature and target combined parameters and the minimum cold capacity supply and demand deviation of each partition as targets; and establishing a lower-layer data center optimization scheduling model by taking the minimum power utilization operation cost of the system and the minimum load migration cost of the IT equipment as a target.

The invention discloses a machine room water-cooled air conditioner adaptive control method based on big data analysis, which relates to the technical field of adaptive regulation and control, is used for improving the problem of overlong response time caused by fixed thermal inertia, and comprises the following steps of: performing network deployment on distributed optical fiber sensing, screening environmental data in a water-cooled air conditioner adaptive system by utilizing a sliding time window algorithm, and determining the response time of the water-cooled air conditioner. Noise is suppressed, a thermal inertia compensation mechanism is entered, an LSTM neural network is constructed based on initial system thermal inertia to predict thermal disturbance of a plurality of subsequent control periods, and a space-time alignment compensation matrix is introduced to suppress harmonic oscillation in the thermal inertia compensation mechanism; the predicted thermal disturbance and the chilled water flow in the air conditioner are integrated, a double-closed-loop correction method is used for generating thermal compensation correction, gain switching is conducted on the double-closed-loop correction method, and synchronous alignment correction is conducted on asynchronously-collected data packets through a time sequence alignment engine; and setting a bending window and carrying out characterization fusion to judge an environment data acquisition point and an abnormal acquisition response rate thereof.

The invention provides a big data information processing method and system based on deep learning. According to the method, an energy distribution model is generated by combining a building information model with infrared thermal imaging data, and a dynamic thermal balance equation set is constructed according to the model so as to generate three-dimensional temperature field reconstruction data. Then, thermal resistance parameters in the energy distribution model, heat flow abnormal area coordinates in the three-dimensional temperature field reconstruction data and air conditioning unit current data are analyzed through a deep learning algorithm, and space-time coupling characteristics of equipment power consumption and thermal environment parameters are extracted; then, a priority regulation and control chain of the temperature control equipment is formulated, and multi-stage linkage rules of the fresh air exchange rate, illumination dimming and cold release of the cold storage tank are covered; finally, the chilled water pump phase difference, the air conditioner group operation mode and the emergency lighting power limiting value are dynamically adjusted, and power load stabilization is achieved. According to the technical scheme provided by the invention, the data information processing efficiency and stability of the large building can be improved.

The invention relates to an air conditioner chilled water PID parameter optimization method based on an improved sparrow search algorithm. The method comprises the steps that population initialization, a discoverer updating formula and a follower updating formula in the sparrow search algorithm are improved; a return water temperature difference signal of the air conditioner chilled water system is detected and input to the improved sparrow search algorithm optimization PID controller, and the improved sparrow search algorithm dynamically adjusts the proportionality coefficient, the integral coefficient and the differential coefficient of the PID controller; and the improved sparrow search algorithm iteratively calculates an optimal parameter combination, and the PID controller performs optimal control of the chilled water system based on the optimal parameter combination. The improved sparrow search algorithm carries out population initialization by introducing a reverse learning strategy optimized by a refraction principle so as to enhance the diversity of an initial population; respectively introducing sine and cosine transform and a Cauchy variation mechanism in a position updating stage of the discoverer and the follower; and the PID controller dynamically adjusts Kp, Ki and Kd based on an improved algorithm so as to realize optimal parameter setting.

The invention discloses a heat allocation system based on a heat pump. The heat allocation system comprises a water tank; a water source heat pump; an air source heat pump; a cooling tower; a cooling water circulation system; a hot water circulation system; a chilled water circulation system; an exhaust heat recovery system; wherein the water tank, the water source heat pump, the air source heat pump, the cooling tower, the cooling water circulation system, the hot water circulation system, the chilled water circulation system and the exhaust heat recovery system are connected with one another to realize heat allocation, especially heat allocation of a coating pretreatment electrophoresis production line of a vehicle body or parts forming the vehicle body; according to the system, heat generated when the heat pump / refrigerating unit produces chilled water and / or heat taken away in exhaust air can be recycled and used for heating the heat using tail end. Therefore, the heat which is wasted originally can be fully utilized, the energy consumption is reduced, and the utilization efficiency of energy is improved.

The invention relates to a data center equipment type selection method based on electrical and heating ventilation load linkage, and belongs to the technical field of data center infrastructure design. The model selection method comprises the steps of setting basic data, obtaining the number of machine rooms and the number of IT devices in a single machine room, obtaining the total power of the IT devices, setting an energy efficiency index set value, obtaining the project power consumption scale, obtaining the total cooling load, selecting models of air conditioners at the tail ends of the machine rooms and substations, selecting models of water chilling units, selecting models of heat exchangers, selecting models of cooling water pumps and chilled water pumps and selecting models of cooling towers. And obtaining the total power of the air conditioner, obtaining an energy efficiency index accounting value, and carrying out PUE verification. According to the method, through closed-loop iteration of electrical load-heating and ventilation load-PUE, model selection and quantity selection of all key equipment are completed at a time; professional splitting, repeated adjustment and modification and capacity redundancy caused by traditional serial design are avoided, the design period is remarkably shortened, and initial investment is reduced.

The invention relates to an urban rail transit station ventilation and air conditioning system cooperative control method, device and equipment based on dynamic energy efficiency optimization and a medium. Constructing an energy efficiency model of the ventilation and air conditioning system by combining the energy efficiency curved surface characteristics of the water chilling unit and the cubic law power consumption characteristics of the frequency conversion equipment; an intelligent optimization algorithm is adopted, a dynamic scaling temperature feasible domain of chilled water of a water chilling unit and a frequency interaction limitation domain of frequency conversion equipment are constructed, the feasibility of a temperature set value is kept, meanwhile, coupling constraints in the frequency conversion equipment are decoupled, and an optimal parameter combination is solved; and the optimal parameter combination is issued to an execution layer, the actual energy efficiency ratio deviation of the system is monitored in real time, and energy efficiency model parameter re-calibration or iterative optimization is triggered according to a deviation threshold. Therefore, the overall energy efficiency of the ventilation air conditioning system is stably improved, and efficient operation under the dynamic working condition is achieved.

The invention discloses a chilled water system operation control method, a chilled water system and a storage medium. Relates to the technical field of refrigeration equipment, and the method comprises the steps that according to obtained operation parameters of a chilled water system, predicted chilled water flow is obtained, the predicted chilled water flow corresponds to a preset time period, and the preset time period is a preset time period after the current time; on the basis of the predicted chilled water flow and system configuration information, initial operation parameters in the preset time period are determined; and according to the operation initial parameter and the predicted chilled water flow, an operation target parameter is determined, and the chilled water system is controlled to operate by adopting the operation target parameter in the preset time period. And fine control of the chilled water system in the preset time period is realized. On the premise of meeting system operation requirements, energy consumption is reduced, operation efficiency is improved, and meanwhile system instability caused by parameter adjustment lag is reduced.

The utility model provides a heat exchanger assembly and an air conditioner, the heat exchanger assembly comprises an evaporator assembly and a surface air cooler assembly, refrigerant is introduced into a heat exchange tube of the evaporator assembly, chilled water is introduced into a heat exchange tube of the surface air cooler assembly, and the evaporator assembly and the surface air cooler assembly are connected into a whole. The evaporator assembly is located at the upstream position of the surface air cooler assembly in the air flowing direction, when chilled water is mainly used for refrigeration, the surface air cooler assembly works, the evaporator assembly does not work, and air passes through the evaporator assembly to be not cooled and then passes through the surface air cooler assembly to be cooled. According to the utility model, the refrigeration effect is not worsened under the condition that the energy efficiency and the refrigeration performance are considered at the same time, and the required refrigeration effect is ensured.

The invention provides a multi-target control strategy optimization method for intelligent cold release scheduling of a chilled water storage system. The method comprises the following steps: acquiring chilled water storage system data; constructing a multi-input time sequence regression model to obtain a cold load prediction mean value and a standard deviation sequence; acquiring a corresponding electricity price per hour and a building cold and hot inertia coefficient sequence, and generating a weighted risk score value; constructing an optimization objective function with the lowest electricity price cost and the lowest risk to generate an optimal cold release strategy sequence; and executing the optimal cold release strategy sequence, deploying the optimal cold release strategy sequence into a real chilled water storage system, and performing boundary management and limited optimization fine tuning on the strategy according to the real-time state of the system in the operation process. According to the method, the problems of multi-link splitting, poor strategy robustness, execution disjunction and the like in the current cold and heat scheduling system are solved, and the method has good implementability and systematic innovation value.

The present application relates to a system and method for supplying cascade energy using waste heat from a power plant during production. The waste heat from the power plant during production is connected to a heat source inlet of a lithium-bromide absorption chiller and to a waste water inlet of a waste water heat exchanger, and a heat source outlet of the lithium-bromide absorption chiller is connected to the waster water inlet of the waste water heat exchanger and to a water return port of the waste heat from the power plant during production; the lithium-bromide absorption chiller is connected to a chilled water intake line, a chilled water output line, a cooled water intake line and a cooled water discharge line, the cooled water discharge line is connected to a clean water side inlet of the waste water heat exchanger, and a clean water side outlet of the waste water heat exchanger is sequentially connected to a water purification device and a hot water tank; the clean water side inlet of the waste water heat exchanger is connected to a heating water return line, and the clean water side outlet of the waste water heat exchanger is connected to a heating water supply line; and a water outlet of the waste heat from the power plant during production is further connected to a heat source inlet of a solution regenerator, and a waste water outlet of the solution regenerator is connected to the water return port of the waste heat from the power plant during production. The system of the present application has high energy utilization efficiency, thus effectively expanding the range of waste-water waste heat utilization.

The invention discloses a fuzzy algorithm-based control method and system for parallel variable-frequency water pumps of a refrigerating unit. The method comprises the following steps of: acquiring operation data of the refrigerating unit; and determining an input variable according to the operation data, determining an output variable through a fuzzy algorithm based on the input variable, and adjusting the refrigerating unit parallel variable frequency water pump according to the output variable. The fuzzy algorithm is adopted to control the refrigerating unit parallel variable frequency water pump, and the chilled water supply and return water temperature difference, the chilled water flow deviation, the cooling water supply and return water temperature difference, the cooling water flow deviation and the refrigerating unit load rate serve as input variables; the chilled water pump frequency adjustment amount and the cooling water pump frequency adjustment amount are used as output variables, so that the chilled water pump frequency adjustment amount and the cooling water pump frequency adjustment amount can be smoothly adjusted according to the current state and the change trend of the system, oscillation and overshoot are effectively reduced, the system runs more stably, and the service life of equipment is prolonged.

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 water chilling unit operating under extreme working conditions, which comprises a fan and a finned condenser at the upper part and a refrigerating unit at the lower part, and the refrigerating unit is provided with a refrigerant circulating system and a chilled water circulating system; the refrigerant circulating system comprises a compressor, a finned condenser, an electronic expansion valve and a shell and tube evaporator which are connected in sequence; the chilled water circulation system comprises a water pipe, a water pump and an evaporator which are connected in sequence; the electronic expansion valve is connected with the drying filter cartridge and the shell and tube evaporator, the drying filter cartridge is connected with the liquid storage tank, and the shell and tube evaporator is connected with the gas-liquid separator. According to the invention, PWM and stepless regulation multi-dimensional cooperative control are used, so that stable operation is realized when the energy efficiency in a wide temperature range is suddenly reduced; on the high-temperature side, the condensation fan adopts a stepless speed regulation technology; and the low-temperature side is switched into a PWM period adjusting mode.

The invention provides a wind-water linkage energy-saving control system for an urban rail transit underground station. The wind-water linkage energy-saving control system comprises a management layer; the control layer is divided into three relatively independent and expandable cooperative control subsystems capable of independently acquiring information according to the type of equipment, the control layer comprises a large system cooperative control subsystem, a small system cooperative control subsystem and a water system cooperative control subsystem, and the cooperative control among the cooperative control subsystems is completed by the management layer platform; a control strategy of the urban rail transit underground station wind-water linkage energy-saving control system comprises a system calendar scheduling strategy; a system control strategy; comprising a large system air conditioner adding strategy, a large system air conditioner reducing strategy, a water system starting working condition setting strategy, a water system operation working condition switching strategy, a cooling water temperature difference optimization setting strategy, a chilled water temperature difference optimization setting strategy, a most unfavorable tail end pressure difference setting strategy, a most unfavorable tail end pressure difference control strategy and a water chilling unit adding strategy. A water chilling unit reduction strategy, a water chilling unit alternation strategy and a water chilling unit operation load balancing strategy; and the system cooperative control comprises one-key startup of the water chilling machine room, one-key shutdown of the water chilling machine room, one-key startup of the water chilling unit, one-key shutdown of the water chilling unit, automatic addition of the water chilling unit and automatic reduction of the water chilling unit.

A hydronic system, of the present disclosure, includes a control unit including a memory comprising stored instructions and a processor configured to execute the stored instructions to cause the control unit to perform at least monitoring, a volume of hot water in a hot storage tank via a first set of flowmeters, monitoring, a volume of chilled water in a cold storage tank via a second set of flowmeters, monitoring, a first outlet temperature of the hot water within the hot storage tank via a first temperature sensor and a second outlet temperature of the chilled water within the cold storage tank via a second temperature sensor, and operating, a heat pump unit for recharging the hot water and the chilled water within the hot storage tank and the cold storage tank respectively. The heat pump unit is operated when a respective predetermined limit reaches for anyone of identified parameters.

The utility model relates to the technical field of cold storage and energy storage, and provides a chilled water storage refrigerating system. Comprising an energy storage pool, a heat exchanger assembly and a water chilling unit. The energy storage pool is connected with the cold release side of the heat exchanger assembly to form a circulation loop. The user side of the heat exchanger assembly is connected with the user side to form a circulation loop. The chilled water side of the water chilling unit is connected with the energy storage pool to form a circulation loop, and the chilled water side of the water chilling unit is connected with the user side to form a circulation loop; the cooling water side of the water chilling unit is connected with the cooling tower assembly to form a circulation loop. The water chilling unit has the beneficial effects that the water chilling unit stores energy for the fire-fighting energy storage water pool and / or the newly-built energy storage water pool in the electricity utilization trough period at night, and the stored energy in the fire-fighting energy storage water pool and / or the newly-built energy storage water pool exchanges heat with the heat exchanger assembly to refrigerate the user side in the peak period and the peak period in the daytime; after the stored energy in the fire-fighting energy storage water pool and / or the newly-built energy storage water pool is used up, the water chilling unit and the user side are used for heat exchange refrigeration, and the refrigeration cost is reduced.

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 air conditioning refrigerating systems, in particular to a chilled water cascade energy-saving control method and a chilled water cascade energy-saving control system for a chilled water cold storage system, and the method comprises the following steps: calculating low-temperature load cold storage capacity, medium-temperature load cold storage capacity and high-temperature load cold storage capacity of a cold storage container; according to a local power grid policy, determining peak, flat and valley periods; determining low-temperature, medium-temperature and high-temperature load cooling time; collecting hourly cold loads and corresponding meteorological parameters of low-temperature, medium-temperature and high-temperature chilled water when the refrigerating system operates; training to obtain a model; according to meteorological parameters of hourly weather forecast of the next day; summing the predicted high-temperature, medium-temperature and low-temperature hourly loads of the next day in the peak period of the power grid; a low-temperature load cold release strategy is formulated; formulating a medium-temperature load cold release strategy; a high-temperature load cold release strategy is formulated; and according to the determined low-temperature, medium-temperature and high-temperature cold release time, opening and closing of each valve of the system are controlled, and each cold release process is completed.

The invention discloses an artificial habitat cold water supply scheme engineering evaluation method and system, and relates to the technical field of coral cultivation. According to the scheme, a Pareto optimal solution set is solved by adopting a multi-objective optimization algorithm, and the blindness and subjectivity of artificial experience selection are avoided by introducing environmental parameters to dynamically adjust the weights of time and space target values, so that a target cold water supply scheme can adapt to the requirements of different coral culture areas, and the coral culture efficiency is improved. And the final implementation effect of the coral reef artificial habitat cold water supply project and the coral protection efficiency are improved.

The invention relates to the technical field of energy-saving optimization of central air conditioners, and discloses an energy-saving optimization control method for a chilled water system of a central air conditioner, which comprises the following steps: establishing a target function to construct an energy consumption model based on the composition of chilled water circulation and cooling water circulation modules; constraint conditions are established based on chilled water circulation and cooling water circulation module optimization; calculating parameter settings in the chilled water circulation and the cooling water circulation based on a binary particle swarm algorithm, and performing optimization; according to the central air conditioner chilled water system and the energy-saving optimization control method, energy-saving optimization control over the chilled water system is achieved on the basis of optimized parameter setting, and by constructing energy consumption models of all modules, target functions and constraint conditions needed by optimization are established; and according to a binary particle swarm algorithm, calculating and optimizing parameter setting in a chilled water circulation and cooling water circulation module in the system, and based on optimized parameters, automatically adjusting operation parameters of the chilled water system in a load period so as to realize an effect of efficient and energy-saving optimization control on the system.

The invention discloses an air conditioning system photovoltaic capacity dynamic regulation and control method and system considering a flexible load, and the method comprises the steps: collecting system operation data, building an energy consumption mathematical model of main energy consumption equipment of a heating ventilation air conditioning system, developing supply and demand double-side prediction model algorithm software, and building a photovoltaic capacity dynamic tracking model prediction control cost function; according to the method, the flexible load-considered air conditioning system photovoltaic capacity dynamic tracking model prediction control dynamic regulation and control system is obtained, the chilled water supply temperature, the chilled water flow and the cooling water return temperature are subjected to optimization calculation, feedback correction is carried out on the system output cooling capacity, and automatic regulation and control are achieved. According to the method, the demand side air conditioner load consumption curve dynamically tracks the change of the photovoltaic power generation curve, the system energy consumption cost is minimized, power grid fluctuation caused by current large-scale use of solar energy is considered, and the method is of great significance to improvement of power grid stability and realization of building demand response.

The invention provides a dynamic multi-target intelligent optimization control method for a converter station valve cooling system, and belongs to converter station water cooling systems. By establishing the dynamic model of the converter station valve cooling system and the dynamic weight distribution network based on the attention mechanism, real-time and accurate control over the converter station cooling system is achieved, and the efficiency and stability of the converter station cooling system are improved. The method comprises the following steps: firstly, describing a heat exchange process between cooling water and a converter valve and dynamic characteristics of fluid flow through energy conservation, mass conservation and momentum conservation equations, then, revealing a coupling relationship between flow dynamic change and pump, valve and pipeline characteristics by analyzing dynamic behaviors of flow and pressure of the cooling water, and finally, analyzing the dynamic characteristics of the flow and the pressure of the cooling water. A dynamic weight distribution network based on an attention mechanism is constructed, time-varying demand adjustment of multi-target weights is realized, and intelligent weight adjustment is realized through offline training and online fine adjustment.

The invention relates to a group control optimization method and system for a cold station system, and belongs to the technical field of automatic control, and the method comprises the steps: carrying out the minimum optimization of a first-stage group control optimization objective function through a mixed integer programming algorithm with the start-stop state of each device as an optimization variable during the operation of the cold station system, and obtaining a second-stage group control optimization objective function; the start-stop state of each device of the cold station system is controlled during first-stage group control; and on the basis of the first-stage group control, performing minimum optimization on the second-stage group control optimization objective function by taking chilled water outlet water temperature, chilled pump frequency, cooling pump frequency and cooling tower frequency as optimization variables through a model prediction control algorithm, and controlling the operation state of operation equipment when the cold station system is in the second-stage group control. Therefore, energy consumption is reduced as much as possible and energy efficiency is improved.