722 results about "Thermal comfort" patented technology

The invention provides a BIM-based building energy consumption simulation analysis optimization method and system, and relates to the technical field of building energy consumption, and the method comprises the steps: building a building energy consumption digital twinborn model through integrating a building information model (BIM) and real-time operation data, such as indoor and outdoor temperature, equipment operation, electrical load and personnel density, collected by an Internet of Things sensor. An LSTM and GNN mixed deep learning algorithm is used to predict building energy consumption, and clustering analysis is used to identify an energy consumption abnormal mode and a peak period. And finally, based on a reinforcement learning algorithm, constructing an energy consumption optimization model taking total energy consumption minimization as a target and taking indoor thermal comfort and air quality as constraints, outputting equipment optimization operation parameters such as air conditioner temperature, fresh air volume, illumination brightness and the like, and realizing intelligent optimization control of building energy consumption, so that the building energy consumption is reduced, and the building operation efficiency is improved.

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

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses a vehicle seat self-adaptive adjusting method and system based on environment perception and a medium. The method comprises the steps that physiological state data and human body image data of a target passenger and cabin environment data of a target cabin are obtained; determining body surface temperature and humidity, heart rate parameters and human body metabolic rate of the target passenger according to the physiological state data, and identifying clothing thermal resistance parameters of the target passenger according to the human body image data; determining a human body thermal comfort index of the target cabin according to the human body metabolic rate, the clothing thermal resistance parameter and the cabin environment data; inputting the body surface temperature and humidity, the heart rate parameter and the human body thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain a target thermal comfort demand of the target passenger; and performing heating adjustment and / or ventilation adjustment on the seat of the target cabin according to the target thermal comfort demand. The riding comfort and riding experience of the user are improved, and the method can be widely applied to the technical field of vehicle control.

The invention provides an energy-saving and carbon-reducing multi-objective optimization method and device for a cold source system of a high-speed rail station, and the method comprises the steps: building an air-conditioning cooler system load prediction model and a human body comfort model based on the historical data of an air-conditioning cooler system, the historical data of the internal and external environments of the high-speed rail station, and the thermal comfort evaluation historical data of the internal environment of the high-speed rail station; a multi-objective optimization algorithm is established with minimization of the sum of the air conditioner refrigerator system load and the human body comfort as an optimization objective, and the weight of the air conditioner refrigerator system load and the human body comfort can be adjusted according to real-time data of the internal environment and the external environment of the high-speed rail station; and a multi-objective optimization algorithm is solved, and cold machine optimization control is carried out according to the air conditioner cold machine system load solution value and the air conditioner cold machine system load current value, so that the reliability of energy-saving and carbon-reducing multi-objective optimization of the high-speed rail station cold source system is effectively improved.

The invention is suitable for the technical field of automobile intelligent control, and provides an air conditioner seat collaborative cooling control method based on a neural network, which comprises the following steps of: constructing a human body-environment comfortable temperature original index model by collecting a large amount of human body and environment temperature distribution data in advance; on the basis, data acquisition and model comparison are carried out on the real-time human body temperature, the environment temperature and the temperature in a passenger compartment before the passenger gets on the bus, so that the heat comfort in the bus is pre-adjusted most reasonably before the passenger gets on the bus, and a method for cooperatively controlling ventilation and heating of an air conditioner and a seat is utilized; the two are combined to improve the thermal comfort of the passenger and the passenger compartment. And meanwhile, real-time sensing data is quickly processed when a passenger feels uncomfortable in temperature during riding and adjustment is carried out, the body temperature of the passenger and the ambient temperature of a passenger compartment are recorded and are input into a model for correction, so that the body comfort requirement of the passenger is met in a more personalized manner, and the comfort of the passenger in the vehicle running process is improved.

The invention discloses an indoor environment performance optimization design method and system for a tall and large space building based on CFD and a multi-objective optimization algorithm, and the method comprises the steps: building a parameterized analysis model through combining with a BIM model, carrying out the digital simulation analysis of an indoor light environment and energy consumption through employing Honeybee Radio and Honeybee Entry plug-ins in Rhino and Grasshopper environments, determining optimization parameters and objectives, and carrying out the optimization of the indoor environment and energy consumption. And a BIM model and an IDF file corresponding to the optimal schemes are exported, the uniformity of the indoor airflow organization is evaluated based on FFD software, and parameters of the optimal schemes are determined. Compared with a traditional indoor environment optimization method, the high and large space building indoor environment performance optimization and evaluation method is efficient, accurate and economical, and has higher accuracy, higher adaptability and wider applicability. Natural lighting, building energy consumption and indoor three-dimensional thermal comfort can be considered at the same time, and optimization of the indoor environment performance of the tall and large space building is achieved.

Changing environmental characteristics of an enclosure are controlled to promote thermal comfort, health, wellness, and / or performance for occupant(s) of the enclosure using sensor data, three-dimensional modeling, physical properties of the enclosure, and machine learning (e.g., Artificial Intelligence).

The invention relates to an intelligent energy-saving method for a large building refrigerating system based on a large language model, and belongs to the field of intelligence of large building refrigerating systems. A reinforcement learning framework is constructed, a multi-dimensional state space formed by building thermal loads, equipment operation states and thermal environment parameters is defined, and a dynamic reward mechanism is designed by taking improvement of energy efficiency, guarantee of thermal safety constraints and maintenance of thermal comfort as optimization objectives; developing a word embedding model special for the refrigeration field and a cross-modal decision conversion model, and realizing bidirectional analysis of a natural language instruction and a physical control parameter through feature coding and embedding mapping; and performing low-rank adaptive fine tuning on the pre-trained large language model based on the reinforcement learning experience set, and constructing a closed-loop verification system. Generalized migration and multi-target collaborative decision-making of equipment control strategies are achieved, the self-adaptive regulation and control capacity of a refrigeration system under dynamic loads is improved, and a universal intelligent solution for energy efficiency optimization, thermal safety and comfort is provided for a large building.

The invention relates to the technical field of intelligent building environment control and energy management, in particular to a thermal comfort regulation and control method and device based on visual perception and global sensitivity analysis, and constructs an attribute recognition model based on collaborative optimization of a lightweight visual perception network and an attention mechanism. High-precision dynamic capture of human body attributes is realized through the model; human body metabolic rate information is calculated based on the recognized human body attributes, then a thermal comfort model based on global sensitivity analysis and a hybrid intelligent algorithm is constructed, human body thermal comfort is calculated through the model, and finally working parameters of an air conditioner or a heat pump are adjusted based on the human body thermal comfort. The contradiction between environment parameter real-time response and energy consumption optimization is effectively solved, and an innovative solution is provided for building energy conservation and personalized thermal comfort management.

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

The invention discloses a sleep thermal comfort perception and indoor sleep environment regulation and control method and device, and belongs to the technical field of computer vision and heating ventilation air conditioning control, and the method comprises the steps: obtaining video data, physiological data and environment data, which are measured in advance, of a subject during sleep; performing data annotation, feature extraction and data fusion on the video data, the physiological data and the environmental data to make a data set; inputting the manufactured data set into a pre-constructed sleep thermal comfort perception model to obtain a sleep thermal comfort perception prediction result; regulating and controlling the indoor sleep environment according to the obtained sleep heat comfort perception prediction result; according to the method, by predicting the sleep thermal comfort of the person, the operation parameters of the heating ventilation air conditioning system can be correspondingly adjusted, the influence of contact type equipment on a sleeper is avoided, the thermal comfort of the sleeper can be ensured, the energy use efficiency can be remarkably improved, and therefore the total energy consumption of a building is reduced, and energy conservation and emission reduction are achieved.

The invention discloses an intelligent regulation and control method based on perception and dynamic partitioning. In order to solve the problem that an existing public building air conditioning system is difficult to deal with personnel activity intensity difference, emotion fluctuation and space density dynamic change, accurate recognition of personnel behaviors and activity density is achieved by fusing YOLOv5, OpenPose and LSTM networks, and the group emotion state is obtained by combining infrared thermal imaging and expression recognition. Based on behavior-density partitioning and an emotional load correction model, space thermal load distribution is dynamically judged, an NSGA-I multi-objective optimization algorithm is introduced, the air conditioner energy efficiency ratio and the thermal comfort degree are optimized, and differential intelligent control over regular and irregular partitioning is achieved through self-adaptive regulation and control of a meta-reinforcement learning control framework. According to the method, the building energy consumption is effectively reduced, the local comfort degree is improved, good real-time performance and intelligent adjusting capacity are achieved, and the method is suitable for optimization design of an intelligent air conditioning system of a large public building.

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

In order to solve the problems of high energy consumption and unbalanced comfort of a traditional static control strategy of the air conditioning system during environment change, a building three-dimensional model is constructed, and sensor data and energy consumption simulation software are fused to construct a joint simulation environment. The improved extreme learning machine neural network is used for predicting the air conditioner energy consumption and the personnel thermal comfort degree, the network structure and parameters are optimized through the rotary particle swarm optimization, and the prediction precision and generalization ability are improved. And then, in combination with a Newton-Raphson multi-objective optimization algorithm, solving an air conditioner temperature set value optimal dynamic sequence in a rolling manner, and balancing energy consumption and comfort through a Pareto frontier solution set. Through dynamic simulation and data driving optimization, precise regulation and control of the air conditioner are achieved, and the thermal comfort degree is improved.

The invention relates to the technical field of constructional engineering, and discloses an intelligent ventilation and purification method and system for constructional engineering, and the method comprises the steps: 1, determining the concentration of pollutants in a building based on real-time air quality data, and calculating the required ventilation quantity meeting a preset standard; secondly, the fresh air introduction proportion is adjusted according to the indoor and outdoor temperature and humidity difference value so as to optimize energy consumption and thermal comfort balance; 3, according to the personnel distribution density, the local area air supply intensity is set, and the air circulation uniformity is ensured; fourthly, the system operation mode is regulated and controlled based on the working state of the air purification equipment, so that energy consumption is reduced, and the service life of the equipment is prolonged; the fresh air introduction proportion is intelligently adjusted by comparing indoor and outdoor temperature and humidity differences. For example, when the outdoor temperature and humidity are close to the indoor comfortable interval, the fresh air volume is increased to utilize natural cold and heat sources; when the outdoor environment is extreme, the fresh air volume is reduced, the heat recovery device is started, the air conditioner load is reduced, and energy waste is reduced.

The invention provides an intelligent adjustment method for distributed air treatment equipment, and belongs to the technical field of air treatment equipment, and the method comprises the steps: inputting a personnel distribution image into an airflow field reconstruction model based on a graph neural network, outputting three-dimensional space flow field distribution data, calculating a personnel intensity index, and predicting an average thermal sensation index value; an air supply adjustment double-layer game model is established based on the data, the air supply angle and the air supply speed of an electric side supply nozzle and the air supply quantity of a lower air supply outlet are solved, an actuator is controlled to adjust air supply parameters, and the target air outlet temperature and the refrigerant flow of the heat exchanger are calculated through a thermodynamic entropy production minimization heat exchange optimization algorithm; and feedback correction is performed according to the actually predicted average thermal sensation index deviation to form closed-loop control, so that the technical problem that the distributed air treatment equipment is difficult to adjust the air supply parameters in real time according to the personnel distribution dynamic change so as to ensure the thermal comfort and the energy efficiency at the same time is solved.

The invention relates to a method and a system for optimizing indoor thermal comfort of a building based on machine learning, and the method for optimizing the indoor thermal comfort of the building based on machine learning is used for maintaining the indoor thermal comfort of the building in cooperation with indoor temperature control equipment of the building. Comprising the following steps: S1, establishing a BIM three-dimensional information model of a building indoor structure; s2, acquiring indoor environment data of the building by using a sensor, and constructing a digital twin platform in combination with the BIM three-dimensional information model; s3, preprocessing the acquired indoor environment data by using a digital twin platform; according to the method, the indoor environment data of the building is analyzed and predicted by the machine learning technology after being preprocessed, the indoor future environment conditions of the building are accurately predicted, the adjustment data of the indoor temperature control equipment of the building in the future time period are obtained, and therefore the purpose of rapidly and accurately adjusting the thermal comfort degree in each time period is achieved.

The invention discloses a regional cooling system supply and demand side collaborative optimization method based on a master-slave game, and the method comprises the following steps: obtaining meteorological data and date attribute data of a climate region where a selected city is located, determining a typical building type, and collecting building information; constructing a building energy consumption agent model of a user side; calculating a cooling load demand based on the building energy consumption agent model, and constructing a target function of the user side cold purchase cost based on the cooling load demand; constructing a cold supply station operation model; a supply and demand side collaborative optimization model between the cold supply station and the user side building is constructed, and the building energy consumption agent model meets the thermal comfort degree constraint; taking the maximum cold supply benefit of the cold supply station and the minimum building cold purchase cost of the user side as optimization objectives, and performing a master-slave game between the cold supply station and the cold users; and solving to obtain a supply-demand side collaborative optimization result. According to the method, the demand side model is incorporated into the optimization problem to form global optimization, the flexibility of the system can be effectively improved, and the operation stability of the regional cooling system in an extreme scene is improved.

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

According to the method, a set of parameterized simulation optimization design method integrating passive low carbon emission and high comfort is constructed for the design stage of a building scheme, and the method is a building low-carbon optimization design method based on an energy consumption-thermal comfort correlation model and comprises the steps that based on a parameterized building performance optimization model, an energy consumption-thermal comfort correlation model is established; establishing a parameterized model, performing microenvironment simulation by using a plug-in, and collecting data; sensitivity analysis of the parameterized model: carrying out sensitivity analysis by adopting random forest regression, gradient enhanced regression and decision tree regression, and discussing a relationship between a design variable and an environment target; and performing multi-target optimization based on performance simulation to obtain a compromise optimal result under multiple targets. According to the method, corresponding geometric and enclosure structure design parameter strategies can be provided from the aspects of building greenhouse gas emission, indoor and outdoor thermal environment comfort and the like, efficient design iteration and decision support is provided for designers, and sustainable development of green communities is promoted.

The invention relates to the crossing field of biothermodynamics and dynamic environment control, and discloses a space thermal microclimate multi-target regulation and control method, device and system, a medium and a product. The method comprises the following steps: acquiring physiological parameters (including effective dressing thermal resistance and local sweat gland density of posture correction) of a passenger, environment space-time parameters and light environment parameters through a biological characteristic sensor, an environment sensor and an optical sensor; constructing a dynamic human body thermal comfort model library, and adopting a feature coding-parameter decoding mechanism to match a target thermal comfort model; generating a hierarchical regulation strategy based on the difference between the local comfortable temperature and humidity interval and the real-time thermal environment sensing parameter; and the refrigerant circulation system, the fluid circulation system and the optical radiation system are cooperatively controlled to realize local microclimate regulation and control. According to the method, the problems of inaccurate thermal comfort modeling and low multi-system cooperation efficiency in a transient environment are solved, the energy efficiency ratio is improved, and the comfort of passengers in a cabin is remarkably improved.

The invention discloses a thermal comfort monitoring robot system and method based on multi-modal body language feature recognition and fusion, and aims to realize individual thermal comfort monitoring, regulation and control in a dynamic indoor environment. The intelligent mobile platform carries multiple types of environment sensors and visual acquisition equipment, and performs autonomous navigation and acquires environment parameters and human body image data; the multi-modal feature recognition module performs human body detection, posture recognition and identity maintenance on a video stream frame by frame based on a deep learning network, and extracts multi-modal human body features; the data processing and fusion module realizes cross-frame identity consistency through an algorithm and fuses multi-modal human body features and environmental parameters; the thermal comfort calculation module dynamically adjusts a metabolic rate parameter based on a predicted average voting model, incorporates the metabolic rate parameter into a psychological correction factor and calculates an individual thermal comfort index; the three-dimensional visualization and regulation and control module generates a comfort distribution map, is in butt joint with a building environment automatic control system to achieve dynamic regulation and control, and provides support for intelligent building environment management.

The embodiment of the invention discloses an air conditioner control method and device, electronic equipment and a computer readable storage medium. The method comprises the steps that environment information of a space where an air conditioner is located is obtained, and a thermal imaging image of a user in the space is obtained; determining a target feature vector according to the environment information and the thermal imaging image; performing thermal comfort prediction on the target feature vector to obtain a thermal comfort value; and a target operation strategy is determined according to the thermal comfort degree value, and the target operation strategy is an operation strategy for air conditioner control under the thermal comfort degree value. According to the air conditioner control method provided by the embodiment of the invention, the air conditioner can be controlled according to the current thermal comfort value of the user, so that the sensible temperature of the user is closer to the set temperature.

The invention provides an intelligent control system and method for a natural cooling heat exchange ventilation unit, and relates to the technical field of energy-saving fans. The optimal operation mode and the natural cooling availability of a target ventilation unit are determined firstly; the expected heat exchange amount of the natural cold side is determined according to the current indoor heat load demand characteristics and the natural cold available capacity, and then a static adjusting instruction of a natural cold side actuator is determined according to the expected heat exchange amount; according to the heat load demand characteristics and the expected heat exchange amount, the mechanical refrigeration demand cold capacity is determined, and then a dynamic adjusting instruction of a mechanical refrigeration side actuator is determined according to the heat exchange efficiency and the mechanical refrigeration demand cold capacity of the mechanical refrigeration unit under the current working condition; and performing coordination control on the actuators on the natural cooling side and the mechanical refrigeration side based on the static adjustment instruction and the dynamic adjustment instruction. According to the scheme, dynamic cooperative control over natural cooling and mechanical cooling of the natural cooling heat exchange ventilation unit under the variable working conditions can be achieved, and therefore the energy efficiency is optimized on the premise that the requirement for indoor thermal comfort is met.

The invention relates to an electrochromic window multi-scene control method, system and device based on indoor human body thermal comfort and a medium. The method comprises the steps that a perception data set is constructed; based on the sensing data set, performing multi-parameter coupling calculation of solar incidence azimuth deviation constraint and dynamic projection overlapping to obtain a direct radiation component, performing window space coupling analysis and double space compensation calculation to obtain a scattered radiation component, and performing fusion to obtain an individual thermal radiation influence value; determining a scene category based on the perception data set, and establishing a thermal radiation evaluation benchmark by adopting a mode matched with the scene category; and if the thermal radiation evaluation benchmark exceeds a preset threshold value, the required transmissivity of the window faces is obtained through a full-transmissivity radiation distribution method, the corresponding window faces are adjusted to the target transmission gradient according to the required transmissivity, and coupling control over the wind direction and / or power of the air conditioning system is selectively triggered. According to the invention, by constructing a multi-dimensional dynamic sensing system and an intelligent control framework, collaborative optimization of human body thermal comfort and building energy efficiency is realized.

The invention discloses an optimal control method and system for a central air conditioner. The method comprises the steps that a starting instruction is responded; selecting an operation parameter combination from a preset database and sending a test request to the central air-conditioning system; energy efficiency detection results are received, wherein the energy efficiency detection results comprise energy consumption, equipment efficiency and user thermal comfort indexes; determining a next round of test combination based on the detection result and the mapping relation table; and selecting the combination with the highest energy efficiency from the tested combinations as a final result and configuring the final result to the system. Energy efficiency self-adaptive optimization is achieved by establishing the preset database and the mapping relation table in combination with a multi-round test mechanism, the test is intelligently guided according to the divided energy efficiency value interval, blind traversal is avoided, and the search efficiency is improved. A termination test strategy is set to effectively control a period and reduce interference, a load prediction model is introduced to make optimization more targeted, energy waste caused by lagging response is avoided, key parameters are collaboratively optimized, comfort is improved, and energy consumption is reduced.

The invention discloses a central air conditioner control method, system and device, a medium and a program product, and relates to the technical field of data processing and control systems.The method comprises the steps that a central air conditioner system control model is built; the method comprises the following steps: constructing an actor network, and embedding an optimization control model consisting of a global critic network and a local critic network of a GAT (Graphics Attention Transform); and training the optimization control model, and controlling the refrigeration temperature set value and the heating temperature set value of each area according to the current state observation variable of each area by using the trained actor network. A critic network is designed into a global critic network and a local critic network, the problem that regional thermal comfort awards and central air conditioning system total energy consumption awards conflict with each other in the training process is relieved, a graph attention network is fused into the global critic network, coupling information in the system is fully extracted, and the optimization control performance is improved.

The invention discloses a multi-objective optimization method for the form of an urban block, and the method comprises the following steps: S100, constructing a parameterized model, carrying out the experimental design, defining a group of geometric parameters used for describing the form of the urban block, and generating a plurality of sample points in the value range of the geometric parameters through employing a sampling method; s200, bidirectional coupling numerical simulation is executed, a data set is generated, at least one thermal comfort evaluation index and one air quality evaluation index are output, and therefore the data set containing input parameters and output indexes is formed; step S300, constructing and verifying a machine learning agent model to obtain an agent model capable of rapidly predicting the evaluation index; step S400, executing multi-objective optimization search, and automatically searching to enable the thermal comfort evaluation index and the air quality evaluation index to simultaneously achieve a better Pareto optimal solution set; and step S500, analyzing and outputting an optimization result, extracting an optimization value range of the geometric parameters, and generating a design guide.

According to the data center cluster two-stage energy sharing method based on the multi-element load comprehensive demand response, the first stage is a day-ahead optimization stage, uncertainty probability distribution of wind power, photovoltaic power generation and loads is fully considered in the stage, and a shared energy storage (SES) optimization scheduling method based on a conditional worst-at-risk value (WCVaR) is constructed. The second stage is a real-time optimization stage: firstly, designing a novel point-to-point (P2P) transaction mechanism based on an electricity and heat supply-demand ratio so as to realize joint sharing of electricity and heat between DCPs; then, a refined IDR model is provided, the model considers the space-time transferability of data loads and the flexibility, the thermal hysteresis effect and the thermal comfort of the household appliances, and indexes such as the efficiency improvement ratio are introduced to evaluate the IDR model; and finally, constructing a benefit function of the DCCO and the DCPs.