180 results about "Cooling load" patented technology

The invention is suitable for the technical field of intelligent control, and discloses a water cooling control method, system and device and a medium. Based on the preprocessed data, constructing a thermal load trend prediction model based on LSTM to obtain a cold load prediction value; based on the cold load prediction value, a PID adjusting signal is generated in combination with the temperature error value; based on the PID adjusting signal, a water pump target frequency instruction is obtained in combination with the cooling load predicted value; based on the water pump target frequency instruction, a valve target opening instruction is obtained in combination with the actually-measured water flow; based on the valve target opening instruction, the fault alarm and emergency instruction is generated, the preset emergency measures are executed, the PID control parameters are updated, the performance evaluation report and the feedback information are obtained, and the response speed, the operation stability and the autonomous decision-making capacity of the water cooling system under the dynamic load and abnormal working conditions are 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 the technical field of electrical performance testing, in particular to a method and system for measuring loss of an inductance winding, and the method comprises the following steps: applying a direct current to a measured inductance winding, synchronously monitoring the temperature, and multiplying a pre-measured direct current resistance value by a direct current value to obtain a direct current loss power value. The direct current is applied to the inductance winding, the temperature is monitored synchronously, the direct-current loss power is obtained by combining the direct-current resistance value and the current value, the temperature curve in the initial stage is extracted, the temperature rise rate is calculated, and therefore the winding heat capacity parameter is obtained, loss measurement can be closely associated with thermal response, and the measurement accuracy is improved. Introduction of a heat capacity factor is added in loss calculation, dynamic characteristics of a winding material on a thermodynamic level can be reflected, instantaneous current with load step change is applied after an inductor is cooled, an instantaneous loss power curve is generated by collecting a voltage sequence and a current sequence at the same time, and loss calculation is not limited to average value or steady state measurement any more.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention discloses a data center dynamic air-conditioning load rapid calculation method, which comprises the following steps of: 1, summarizing data center cold load influence factors as an input condition of a calculation model; 2, constructing a standardized data center reference model based on building energy consumption simulation software; 3, based on the building geometrical characteristics and thermal performance of the target data center, correcting the enclosing structure load of the reference model by adopting an area correction method, and constructing an enclosing structure load rapid calculation model; 4, automatically resetting an internal heat source load calculation result, and constructing an internal heat source load rapid calculation model; 5, correcting the fresh air load of the reference model by adopting a volume correction method based on the geometric characteristics of the target data center building, and constructing a fresh air load rapid calculation model; and step 6, forming a target data center dynamic air-conditioning load rapid calculation method, and obtaining a hourly cooling load prediction result required for covering the whole year or a design stage.

The invention relates to the technical field of ground source heat pump control, in particular to an intelligent cooperative control system for a ground source heat pump system and building energy management. The method comprises the steps that a data collecting and preprocessing unit collects multiple types of data in real time and preprocesses the data to form system state vectors; the load and power generation prediction unit predicts future cooling and heating load and photovoltaic power generation power of the building through a machine learning regression model and a time sequence prediction model; the operation optimization unit generates an optimal operation strategy of the ground source heat pump based on a depth deterministic strategy gradient algorithm; and the multi-source energy coordinated control unit realizes multi-device coordinated control through multi-target mixed integer linear programming. Through combination of prediction data and an optimization algorithm, a dynamic matching relation between load fluctuation and energy supply is fully considered, and the problems of soil heat imbalance and unstable comprehensive energy efficiency ratio of the ground source heat pump system in a traditional fixed parameter operation mode are solved.

The invention relates to the technical field of energy management, and discloses a thermal management system energy consumption optimization method based on a lightweight neural network, which comprises the following steps: step 1, collecting operation data and environment information of an energy storage system, constructing a unified feature input set, performing timestamp alignment and normalization processing on the collected operation data and environment information, and obtaining a feature input set; periodic time features are extracted, and a structured feature matrix used for model input is formed; step 2, based on the structured feature matrix, inputting the structured feature matrix into a long-term trend prediction model to predict future charge and discharge starting time and deadline; and step 3, extracting a prediction result of the long-term trend prediction model. The dynamic cooling regulation and control strategy based on the real-time operation data and the environment parameters is adopted, the temperature prediction model and the cooling load demand analysis mechanism are established, the operation state of the cooling equipment is adjusted according to needs, and the technical effects of flexible regulation and control according to actual needs and reduction of overall energy consumption are achieved.

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 optimal control method for metering a cold and heat storage air conditioning system in a laboratory, and relates to the technical field of optimal control, and the method comprises the following steps: predicting hourly cold load and heat load in a future preset period, and generating hourly load prediction data; obtaining time-of-use electricity price information, and generating electricity price time period division data; formulating an energy storage strategy based on hourly load prediction data and electricity price time division data; the method specifically comprises the steps that in the valley electricity price period, when it is predicted that a load demand exists in the future peak electricity price period, a cold and heat source module is controlled to operate, energy is stored in a cold and heat storage module till the rated capacity is reached or the valley period is ended, and the temperature and humidity in a laboratory are maintained within a first set range through a tail end processing module in the energy storage period; and formulating an energy release and conventional operation strategy based on hourly load prediction data, electricity price time division data and the real-time energy storage state of the cold storage and heat storage module. The method has the effect of improving the optimization control efficiency.

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

The invention discloses a multi-modal information fusion load prediction method based on a cross attention mechanism. The method is suitable for an integrated energy station energy efficiency optimization scene. The method comprises the following steps: firstly, collecting and preprocessing multi-source time sequence data such as meteorological texts, satellite cloud pictures and temperature and humidity, and extracting high-dimensional semantic representations through texts, images and a time sequence encoder respectively; secondly, setting a modal significance pre-gating mechanism, and adaptively adjusting the importance of each modal feature; a time lag compensation cross attention module is introduced to realize time alignment and semantic association among multiple modes; and finally, realizing unified representation of weather, space and time information through a multi-modal fusion network, and outputting a cooling load prediction result. According to the method, the dynamic change rule of the cooling load of the comprehensive energy station can be accurately described under the complex meteorological condition, the prediction precision and stability are remarkably improved, and technical support is provided for cold machine starting and stopping, energy storage scheduling and demand response optimization of the comprehensive energy station.

The invention discloses a data center multi-cold-source intelligent regulation and control method and system based on characteristic temperature prediction, and the method comprises the steps: building a machine room characteristic temperature model through an entropy method, and obtaining a machine room characteristic temperature value through calculation; establishing a machine room cooling load and characteristic temperature prediction model based on the characteristic temperature value and the historical operation data; constructing an energy consumption mathematical model of main energy consumption equipment of the refrigeration system; establishing a cost function taking cold energy supply and demand balance as a constraint condition; performing rolling optimization on the cost function by using a particle swarm optimization algorithm, and solving to obtain an optimal control sequence of the chilled water supply temperature and the chilled water flow; and the field control cabinet transmits a water supply temperature setting signal to the refrigerator control cabinet for execution, transmits a flow setting signal to the flow control valve for execution, and realizes automatic optimization regulation and control of the refrigeration system through real-time feedback correction. On the premise that the temperature of the data center machine room is stable, the energy consumption of the refrigerating system is remarkably reduced, and the operation efficiency and the intelligent level of the system are improved.

The invention provides an equipment control method and device in an air conditioner refrigeration system and related equipment, and relates to the technical field of air conditioner energy saving. The method comprises the steps that in response to a received cooling load prediction result of the air conditioner refrigeration system in a future time period, energy consumption models of multiple target devices in the air conditioner refrigeration system are obtained; on the basis of the energy consumption model and the operation constraint condition of each target device, executing multiple rounds of solving processes on a target function which is used for representing the total energy consumption of the air conditioner refrigeration system and comprises to-be-solved operation parameters of each target device, the operation parameter combination is obtained in the last round of solving process and enables the objective function to be minimized in the future time period; and on the basis of the obtained operation parameter combination, the operation of each cold source device, the rotating speed of the fan and the opening degree of the adjusting device are controlled in the future time period, so that all the devices in the system are synergistically energy-saving, the energy efficiency loss of the system caused by optimization of a single device is avoided, and the overall energy consumption of the system is remarkably reduced.

A cooling system includes one or more cooling panels for affecting a cooling load in an environment. The cooling system also includes a heat exchanger coupled to the one or more cooling panels. Each cooling panel includes a film, a panel body, an inlet port, an outlet port, and a fluid path. The film's radiative properties allow it to achieve a temperature less than an environment temperature. The heat exchanger includes ports that are coupled to the fluid paths of the one or more cooling panels. A control system is used to control flow rates, flow paths, fluid temperatures, component temperatures, cooling rates, component operation, or other aspects of a cooling system. For example, the control system controls or monitors pumps, compressors, fans, valves, sensors, actuators, or a combination thereof.

The goal is to reduce the heating load in winter and the cooling load in summer. [Solution] The membrane structure building comprises a double membrane that constitutes the exterior of the building and has an inner membrane and an outer membrane having a radiative cooling function, and an air blower / discharge device that switches between an insulating mode in which air is blown into the double membrane to form an air layer between the inner membrane and the outer membrane, and a solar radiation shielding mode in which air is discharged from the double membrane to bring the inner membrane and the outer membrane into contact.

The application discloses a building cold load cycle prediction method based on boundary feature protection and an HOA-LightGBM model, and belongs to the field of building intelligence, and comprises the following steps: S1, data acquisition and cleaning; S2, GCMWSG filtering for smoothing processing; S3, selecting evaluation indexes; and S4, constructing an HOA-LightGBM hybrid model and comprehensive evaluation. The application adopts the building cold load cycle prediction method based on boundary feature protection and the HOA-LightGBM model, and by introducing a periodic boundary protection mechanism, effectively solves the boundary distortion problem of traditional MWSG filtering when processing periodic air conditioning load data. Experimental results show that the data processed by GCMWSG retains the load characteristics of start and stop moments, and significantly improves the performance of the prediction model; and the HOA-LightGBM hybrid model provided by the application solves the problems of low parameter optimization efficiency and insufficient prediction accuracy of a shallow model by deeply fusing meta-heuristic algorithms and gradient boosting frameworks, and provides an efficient and reliable technical scheme for real-time prediction of building cold load.

The application belongs to the technical field of combined heat and power supply, and particularly relates to a gas-steam combined cycle waste steam heat supply system, which comprises a gas turbine, a first heat exchange part connected with the gas turbine, a steam turbine connected with the first heat exchange part, a second heat exchange part and a cooling mechanism connected with the steam turbine, a user heat supply part connected with the second heat exchange part, a first valve arranged between the steam turbine and the second heat exchange part, a second valve arranged between the steam turbine and the cooling mechanism, and the cooling mechanism and the second heat exchange part being connected with the first heat exchange part. The application can realize large-area heating without extracting high-quality steam and improving the steam turbine exhaust parameter, and the second heat exchange part can bear part or all of the cooling load of the cooling mechanism, so that the power consumption of the cooling mechanism is reduced or zero, energy is saved, and the overall efficiency of the combined cycle is improved.

The invention discloses an intelligent energy-saving management control system for a central air conditioner. The invention relates to the technical field of air conditioner energy-saving management, which comprises an acquisition assembly, a processing assembly, an analysis assembly and a management assembly, and the analysis assembly comprises a load calculation module, an energy efficiency correction module and a compressor management module. According to the method, a load calculation module, an energy efficiency correction module and a compressor management module are adopted, an airflow disturbance correction coefficient and real-time solar radiation heat gain module, a supercooling degree correction coefficient and a load rate correction term module, a voltage fluctuation correction coefficient and an energy efficiency target module are introduced respectively, and a dynamic calculation system is constructed; the problems that traditional cooling load calculation neglects airflow dynamics, energy efficiency evaluation is disjointed with rated working conditions, frequency control accuracy is insufficient and the like are solved, real-time response dynamic changes of the cooling load, accurate reflection of the actual efficiency of equipment and optimal balance of the load and the energy efficiency are achieved, and an accurate reference is provided for energy supply adjustment and frequency control.

This invention provides a dehumidification device and its control method. The method includes: acquiring the inlet temperature and humidity of the processed air, the target supply air dew point, the processed air flow rate, the air conditions before and after the first-stage impeller, the air conditions before and after the second-stage impeller, the air conditions of the first-stage regenerated air, the air conditions of the second-stage regenerated air, and the node conditions of the low-temperature cooling node, the medium-temperature heat absorption node, the medium-temperature heating node, and the high-temperature heating node; determining the processed air pre-cooling load, the intermediate cooling load, the first-stage impeller dehumidification load, and the second-stage impeller dehumidification load based on the processed air inlet temperature and humidity, the target supply air dew point, the processed air flow rate, and the air conditions before and after the first-stage impeller and the second-stage impeller; determining the first-stage regeneration heat load based on the first-stage impeller dehumidification load and the first-stage regenerated air condition, and determining the second-stage regeneration heat load based on the second-stage impeller dehumidification load and the second-stage regenerated air condition. Using the above technical solution can improve the reliability, energy efficiency, and control accuracy of the dehumidification process.

This invention belongs to the field of carbon capture, utilization, and storage technology, and relates to a self-sufficient heat source heat pump coupling system and method for regenerating carbon-captured amine solutions. The self-sufficient heat source heat pump coupling system includes a carbon-captured amine solution circulation subsystem, a high-temperature heat pump circulation subsystem, and at least one mid-tower cooling circulation loop. By setting up a liquid collection device, cooling system, pumping and redistribution system in the mid-section of the absorber tower, the heated rich liquid is extracted outside the tower, cooled to the target temperature, and then reinjected, thereby improving the absorbent absorption efficiency, reducing amine solution loss, and decreasing the cooling load within the tower. For the low-grade waste heat generated during the reaction, high-temperature heat pump technology is used to transport it to the reboiler to reach the required heat source temperature. This invention constructs a closed system for internal energy recycling, enabling amine solution regeneration without an external fossil fuel heat source, thereby reducing operating costs and completely eliminating direct carbon emissions during the regeneration process.

This invention discloses a multi-step load forecasting method for integrated energy systems based on Bayesian optimization and anti-overfitting, comprising the following steps: acquiring and preprocessing historical time-series data of the integrated energy system; constructing an initial feature set, using a random forest model to calculate the importance of each feature in the initial feature set, and selecting a core feature subset from the initial feature set according to a preset cumulative importance threshold; defining the hyperparameter search space of the hybrid model; using a Bayesian optimization framework, aiming to minimize the comprehensive loss on the validation set, and determining an optimal hyperparameter combination through iterative search; configuring and constructing the hybrid model according to the optimal hyperparameter combination; independently training the hybrid model for electricity load, cooling load, and heating load respectively, inputting the current time-series features into the prediction model, and outputting multi-step predicted values ​​of each type of load within a specified future time period. This effectively improves the overall accuracy and generalization ability of multi-step load forecasting for the integrated energy system over the next 24 hours.

Improved apparatus and methods for a Smart Rooftop Unit (SRTU) incorporating infrared (IR) thermal imaging and artificial intelligence (AI) for enhanced HVAC performance are disclosed. The SRTU integrates multiple IR cameras positioned strategically on, around, or within the unit to capture comprehensive thermal images, enabling real-time monitoring and predictive maintenance. An AI-driven control system analyzes the thermal images to optimize defrost cycles, heating and cooling loads, and overall system efficiency. The control system interfaces with building management systems (BMS) for dynamic adjustments based on occupancy, air quality, and weather conditions. The modular design of the SRTU facilitates easy assembly, maintenance, and component upgrades. Advanced sensors and environmental monitors provide continuous data to ensure optimal operation and energy efficiency. The methods include steps for data acquisition, AI analysis, and system adjustments to maintain peak performance and extend the lifespan of the SRTU components.

This application discloses an information processing method, computer equipment, and medium for integrated energy systems, belonging to the field of artificial intelligence technology. The method includes the following steps: responding to the input of a training sample set, inputting the training sample set into a multi-task prediction model to obtain a first predicted value for electrical load, a second predicted value for thermal load, a third predicted value for cooling load, and a fourth predicted value for carbon emissions; wherein the training sample set is constructed based on historical data of electrical load, thermal load, cooling load, carbon emissions, and meteorological data; constructing a carbon balance physical constraint loss term; wherein the carbon balance physical constraint loss term is used to measure the deviation between the fourth predicted value and the estimated value of carbon emissions, the estimated value being obtained through a linear coupling relationship; weighted summing of the carbon balance physical constraint loss term and the data-driven loss term to construct a total loss function, and updating the parameters of the multi-task prediction model through backpropagation. This application improves the accuracy of the prediction results.

The invention discloses a sky radiation coupling water chilling unit high-proportion renewable cold supply system and a control method, and belongs to the technical field of building air conditioner cold source systems, and the sky radiation coupling water chilling unit high-proportion renewable cold supply system comprises a sky radiation refrigerator, a water-cooled water chilling unit, a cold storage water tank, a water pump, an electric valve, a controller and a radiation ceiling tail end. According to the system, five working modes are dynamically switched, and efficient coupling utilization of a sky radiation cold source in multiple links of cold storage, cold supply and condenser heat dissipation is achieved; at night, a radiation refrigerator is used for preparing cold energy and storing the cold energy in a cold storage water tank; when a cold load exists in the daytime room and the temperature of the water tank is lower than the designed water supply temperature of the radiation ceiling, the water tank directly supplies cold and continues to accumulate cold; when the refrigerating efficiency of the radiation refrigerator is reduced, only the water tank supplies cold; when the temperature of the water tank rises to be unable to supply cold, the water chilling unit is started to supply cold, and the radiation refrigerator is utilized to dissipate heat for the condenser; and when the refrigerating efficiency of the radiation refrigerator is reduced, the water tank is switched to dissipate heat of the condenser.

The invention belongs to the technical field of building energy supply, and discloses a combined type multi-energy energy supply system which is characterized in that a ground source heat pump unit with higher energy efficiency is preferentially started to bear basic cooling and heating loads through multi-energy optimization scheduling and gradient utilization; when the heating load in winter exceeds the maximum heating capacity of the ground source heat pump unit, the air source heat pump is started, and when the cooling load in summer exceeds the maximum cooling capacity of the ground source heat pump unit, the centrifugal water chilling unit is firstly started and then the air source heat pump is started, so that the annual comprehensive operation energy efficiency of the system is remarkably improved; a cooling tower auxiliary heat dissipation loop is arranged for a ground source heat pump, a control unit monitors the accumulated heat taking and releasing difference value of an outdoor buried pipe, and when the accumulated heat taking and releasing difference value exceeds a preset threshold value, the cooling tower is switched to discharge heat, the soil heat unbalance problem is fundamentally solved, and long-term stable operation of the system is ensured; the energy supply reliability can be improved, the building energy-saving requirement is met, and the high application and popularization value is achieved.

The invention provides a method for controlling a water chiller in a water chilling unit and a control system, equipment, a storage medium and a program product thereof, is used for improving the energy efficiency of the water chilling unit, and relates to the technical field of energy management. The method for controlling the water chiller in the water chiller unit comprises the steps that the load of the water chiller is predicted based on the time characteristic, the weather characteristic and the historical cooling load characteristic of the water chiller; according to the load prediction result and the partial load rate constraint condition, a control sequence used for controlling the water chiller is determined, and the control sequence comprises the starting number and the operation sequence of the water chiller in the water chiller unit; selecting an optimal control sequence from the control sequences according to the cooling-water machine model; and controlling the operation of the cooling-water machine based on the optimal control sequence.

This invention discloses a method for predicting the cooling load of public buildings based on spatiotemporal attention transfer learning, comprising the following steps: collecting multi-source heterogeneous data of the target building and constructing a multimodal spatiotemporal dataset; then filtering and classifying the multimodal spatiotemporal dataset to obtain core features of different building types; calculating the subjective and objective weights corresponding to each core feature, and combining the subjective weights to calculate the dynamic weight vector of each core feature; then weighting the dynamic weight vectors of each core feature, and outputting the temporal features affecting the cooling load value based on the temporal dependency of the cooling load; finally, optimizing the weights and enhancing the features of the temporal features, and predicting the enhanced temporal features through a fully connected output layer to output the predicted cooling load value. This invention can achieve universal adaptation for predicting the cooling load of buildings of various types and improve the prediction stability of building cooling load in long-term prediction.

There is provided a method of controlling an air-conditioning system associated with a building for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, the method comprising: obtaining zone environmental condition information including zone temperature data associated to the zone, and cooling air temperature data associated to an air handling unit associated to the zone; obtaining, from a zone model generator, zone cooling load parameters associated to the zone with respect to a plurality of time periods and a zone thermal dynamic model; obtaining, from a scheduler, a sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods with respect to the zone determined based on a multi-component cost function including a plurality of components relating to the plurality of building performance parameters; determining, based on the zone thermal dynamic model, a sequence of zone controller set-points corresponding to the sequence of optimal cool air supply rates with respect to the zone using the zone cooling load parameters, the sequence of optimal cool air supply rates, the zone temperature data and the cooling air temperature data associated to the air handling unit; and sending the sequence of zone controller set-points to a zone controller for controlling a temperature of the zone.

The invention provides a business complex cold load prediction method based on scene feature driving, and the method comprises the following steps: S1, obtaining the historical operation data, environment historical data and time information of a cold source system, and constructing a feature sequence; s2, performing quantitative coding and preprocessing on the features, wherein quantitative feature dimensions comprise date types, time period information, outdoor temperature and outdoor humidity; the preprocessing comprises the steps of filling missing values in a data set and identifying and correcting abnormal values; s3, dividing scene modes by adopting a K-means clustering algorithm, determining an optimal cluster number K through an elbow rule, and defining a scene of each cluster in combination with feature value distribution; s4, constructing a differentiated sub-scene prediction model for different scenes, and executing cold load prediction by adopting a dynamic training strategy; and S5, inputting feature data in real time, and calling a corresponding prediction model to output a cold load value. According to the scheme, the problem of prediction deviation of a traditional method in a complex scene is solved through division of differentiated scenes, targeted modeling and dynamic adaptation.

The invention provides a control method of a solar photovoltaic power generation direct supply air conditioner and hot water energy storage follow-up device. The control method comprises the following steps that power generation amount data of photovoltaic power generation and energy consumption data of field equipment are obtained, and outdoor environment data are obtained; performing energy consumption statistical analysis according to the acquired generating capacity data and energy consumption data; energy consumption data and outdoor environment data are obtained, and the working states of a host and a cooling tower are controlled according to the cooling load requirement and the outdoor climate condition; according to the flow demand or the cooling capacity demand, the working state of the water pump set is controlled; energy consumption data and outdoor environment data are obtained, and natural cooling supply is adjusted according to outdoor climate conditions and workshop cooling and heating load conditions. According to the invention, through obtaining the generating capacity data of photovoltaic power generation and the energy consumption data of field equipment, energy and cold and hot water supply are reasonably allocated, the self-sufficiency rate of energy is improved, and the overall energy consumption is reduced.