2024 results about "Building energy" patented technology

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

The invention discloses a building energy-saving control method and system based on energy consumption parameters, and relates to the field of building energy saving.The building energy-saving control method based on the energy consumption parameters comprises the following steps that S1, parameters are obtained, and a building energy consumption database is constructed; s2, extracting a historical total energy consumption parameter and a historical block energy consumption parameter of building energy consumption; s3, constructing an energy consumption prediction model, and performing training optimization on the energy consumption prediction model; s4, predicting a future total energy consumption trend and a future block energy consumption trend; s5, presetting an energy consumption management and control database and an energy consumption matching rule, and matching an energy consumption management and control strategy in the energy consumption management and control database; and S6, implementing the energy consumption management and control strategy, and updating the building energy consumption database. According to the method, the historical energy consumption data of the building is utilized to accurately predict the future energy consumption trend, the total energy consumption is predicted in advance and refined to the energy consumption of each specific area, and data support is provided for accurate control.

The invention relates to an intelligent building energy-saving control method and system self-adaptive to environment changes, and belongs to the technical field of intelligent buildings. The energy-saving control method comprises the following steps: acquiring data of sensors distributed inside and outside a building and external weather forecast data; performing space-time alignment processing on the sensor data, generating a time-synchronized multi-dimensional environment data stream, performing anomaly detection processing, and outputting a pre-processed structured environment data set; performing environmental parameter prediction according to the structured environmental data set and the external weather forecast data to obtain an environmental parameter prediction result in a future preset time period, and constructing a multi-objective optimization function; acquiring a real-time power grid carbon emission coefficient and equipment operation state parameters, and generating a dynamic constraint condition set; and solving the multi-objective optimization function based on a hierarchical optimization algorithm, taking the dynamic constraint condition set as an input parameter, and outputting an equipment control instruction and an optimization control decision of an execution priority. The energy consumption can be reduced while the comfort degree is ensured.

The invention discloses an intelligent building energy-saving management method and system based on a digital twinning technology, and relates to the field of digital twinning technologies, and the method comprises the steps: building a three-dimensional building model based on a building information model and three-dimensional laser scanning point cloud data, and generating a twinning database through multi-source equipment data fusion; performing energy consumption simulation modeling according to the lamp dimming parameters, the air conditioner performance curve and the elevator operation log in the twin database to generate an energy consumption reference report; acquiring personnel density and environment parameters, and generating a building environment state matrix mapped with the twin database after space-time calibration; and establishing a hierarchical threshold value control strategy library based on the building energy consumption reference report and the building environment state matrix, and generating an optimization control strategy set through the equipment linkage relation matrix. According to the method, by embedding a Pareto optimal solution screening mechanism and equipment mutual exclusion rule verification, the daily average energy consumption is reduced on the premise that the comfort reaching rate of the optimal control parameter packet is ensured.

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention discloses an efficient cross-season energy storage energy pile, and relates to the technical field of new energy and energy conservation. The problems that in an existing energy storage system, the thermal load prediction error is large, underground thermal diffusion attenuation is caused, the heat exchange capacity is lowered, and the geological adaptability is insufficient are solved. According to the scheme, an LA mixed time sequence prediction model is adopted to optimize load prediction, a distributed optical fiber temperature measurement array and a finite element inversion algorithm are combined to accurately reconstruct a stratum temperature field, and the geological type is identified based on a support vector machine classifier to realize self-adaptive regulation and control of heat exchange parameters; meanwhile, the heat pump power, the circulating pump frequency and the heat charging and discharging rate of the phase change material are optimized through a depth deterministic strategy gradient algorithm and a gradient heat release strategy; the heat exchange efficiency, the long-term stability and the complex environment adaptability of the cross-season energy storage system are remarkably improved, and the energy-saving effect of a building energy supply system is improved.

The invention relates to the technical field of energy management, in particular to a building energy-saving analysis system based on big data. According to the method, through time sequence segmentation of the real-time energy consumption data and the historical load records and in combination with Z-score standardization processing, accurate description of the dynamic fluctuation range of the building energy consumption data is achieved, and interference of seasonal changes and equipment operation fluctuation on an analysis result is effectively avoided. According to the outlier detection algorithm constructed based on multi-dimensional features, the fine granularity level of anomaly recognition is improved through composite analysis of curve slope and power fluctuation. A dynamic threshold self-adaptive mechanism is combined with an operation mode and an equipment state, so that the sensitivity and response adaptation capability of energy consumption data monitoring are enhanced. A multi-dimensional parameter fusion method constructed by an association index and a credibility factor is adopted, and the credibility modeling and risk grade division of the energy consumption data are effectively realized by quantifying the association among the fluctuation amplitude, the duration and the data deviation degree.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention relates to the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

The invention provides a network following type and network constructing type energy storage combined planning method and device, and belongs to the field of power system planning. The method comprises the following steps: in a to-be-planned grid following type energy storage and grid construction type energy storage power system, carrying out joint modeling on a grid following type converter, a grid construction type converter and a fed alternating current power grid, so as to obtain a closed loop transfer function of the power system; based on the closed-loop transfer function, constructing a small-interference stability security domain oriented to energy storage joint planning; and converting the small-interference stability security domain into a small-interference stability security constraint, and further establishing a multi-type energy storage joint planning model to obtain a joint planning scheme of following network type energy storage and constructing network type energy storage. According to the method, the influence of the operation condition of the converter on small interference stability can be considered, power system optimization targets such as new energy consumption and the like are considered, and combined optimization configuration of network following type and network construction type energy storage is realized.

The invention discloses an energy efficiency management system for low-carbon and energy-saving operation of building electromechanical equipment, and belongs to the technical field of building energy saving and intelligent control. The system comprises a data acquisition module, an energy efficiency analysis module, an optimization decision module and an execution control module; the data acquisition module acquires information such as equipment operation parameters in real time through the heterogeneous sensor network and the LoRaWAN edge gateway; the energy efficiency analysis module performs multi-dimensional evaluation on the basis of a dynamic energy efficiency reference algorithm by fusing an equipment energy efficiency ratio, carbon emission per unit area and the like; the optimization decision module adopts a mixed integer nonlinear programming algorithm, simulates working conditions through a mixed digital twin model, and generates a control strategy for collaborative optimization of energy consumption, carbon emission and equipment life; the execution control module dynamically adjusts equipment parameters by using a fuzzy PID composite controller; the method breaks through the limitation of static evaluation and single-target optimization of a traditional system, the comprehensive energy efficiency can be improved by 15%-30%, carbon emission is reduced by 20% or above, the service life of equipment is prolonged, and the credibility requirement of carbon checking is met.

The invention provides an intelligent building energy-saving processing control method and system, and relates to the technical field of industrial automation. The method comprises the following steps: dividing a factory into a plurality of areas, and collecting operation data and energy consumption data of production equipment; constructing a digital twin model comprising an equipment energy consumption physical model and a production process logic model, and establishing an initial energy consumption benchmark; generating a cross-regional scheduling scheme through a hierarchical control architecture, inputting the scheduling scheme into a digital twinborn model for simulation, dynamically adjusting an energy consumption reference and scheduling parameters according to a simulation result, and verifying whether an energy-saving index meets a preset threshold value or not; deploying the optimized scheduling scheme to a physical factory, controlling equipment start-stop and operation parameters through an automatic control system, and updating a digital twin model and an energy consumption benchmark based on real-time feedback data; accurate management and control, efficient scheduling and dynamic optimization of intelligent building production energy consumption are realized, the energy consumption cost is effectively reduced, and the service life of equipment is prolonged.

The invention provides a building energy consumption prediction method based on a large language model, and relates to the technical field of building energy consumption prediction, and the method comprises the steps: obtaining the data of a to-be-detected building; constructing a text prompt based on the data of the to-be-tested building; inputting the energy consumption time sequence data and the text prompt of the to-be-tested building into the trained energy consumption prediction model to obtain a future energy consumption prediction value of the to-be-tested building; wherein a text prompt coding sub-module in the energy consumption prediction model comprises a large language model. According to the method, time sequence embedding and text prompt embedding are aligned based on a cross-modal alignment module in an energy consumption prediction model, energy consumption time sequence data of a to-be-tested building and text prompt are fused, and finally a future energy consumption prediction value of the to-be-tested building is obtained through aligned time sequence embedding. The method has universality in the energy consumption prediction process, and energy consumption prediction of different types of buildings in different regions is achieved.

The invention belongs to the technical field of intelligent building energy management, and particularly discloses and provides a dual-mode cold storage air conditioning system based on human flow dynamic cooperative regulation, which comprises the following steps: acquiring dynamic human flow thermodynamic diagram data in real time, and accurately calculating a multi-region linkage cold load demand value based on a density change correlation coefficient and a heat conduction model; the real-time sensing of the flow density fluctuation and the dynamic refrigeration demand matching are realized; the phase change state of the coolant of the cold storage tank and the power consumption data of the refrigerating unit are collected in real time, the cold storage release proportion, the refrigerating power increment and other key parameters are dynamically adjusted, and the energy utilization rate is increased; the inherent defect of non-uniform cold and heat in zone control is overcome by calculating inter-zone cooling capacity migration parameters; temperature feedback data and a people flow thermodynamic diagram are collected in real time, the proportionality coefficient of the fan rotating speed and the air valve opening degree is dynamically adjusted, a closed-loop learning mechanism is formed, and the matching precision of a temperature control strategy and people flow distribution is continuously optimized.

The invention discloses a commercial building energy monitoring and intelligent control method and device and a storage medium, and belongs to the technical field of building intelligent control, and the method comprises the steps: collecting data, building a nonlinear mapping relation, and generating an energy consumption demand prediction tensor; injecting an adversarial disturbance sample, and evaluating the robustness of the prediction model; in combination with the energy consumption baseline, performing cross confirmation and correction on the prediction data exceeding the threshold value; performing attribution analysis on the corrected energy consumption sequence to generate an energy consumption attribution map; adjusting the solution of a multi-objective optimization function according to the atlas, and generating an optimal cooperative control strategy; and the comprehensive efficiency is used as a reinforcement learning reward, strategy parameters are iteratively updated, and a control knowledge base is formed. According to the method, a closed-loop control framework integrating robust demand prediction, dynamic attribution analysis, collaborative optimization decision and a self-evolution strategy is adopted, intelligent regulation and control of building energy consumption can be realized, and the long-term adaptive optimization capability is improved.

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 discloses a self-adaptive algorithm optimization control system and method for intelligent energy management, and relates to the technical field of energy management, and the method comprises the steps: determining the load change sensitivity according to a load change prediction rate; grouping the devices according to the load change sensitivity and the current load state to obtain a plurality of device load groups; mapping the load change prediction rate and the multi-dimensional dynamic threshold matrix to obtain a target control strategy; determining an initial control parameter according to the target control strategy; performing difference analysis on the operation parameter and the initial control parameter to obtain a control parameter difference value; adjusting the target control strategy according to the control parameter difference value so as to update the initial control parameter; the updated initial control parameters are input into an execution control unit; the adjusted load state of the building equipment is fed back in real time; according to the invention, accurate real-time prediction and control of building equipment load change are realized, and the stability and response efficiency of intelligent building energy management are effectively improved.

The invention relates to the technical field of supercapacitors, and provides a cement-based supercapacitor, a preparation method and application. The cement-based supercapacitor comprises a cement-based electrolyte and two structural electrodes arranged on the two sides of the cement-based electrolyte. The cement-based electrolyte is prepared from cement, alkali, alkali metal salt, water and an additive, wherein the alkali metal salt comprises lithium salt; each structural electrode is composed of foamed nickel loaded with nitrogen-doped reduced graphene oxide. The lithium salt is added to construct an efficient ionic conductive network, so that the ionic conductivity of the cement-based electrolyte is enhanced, and the electrochemical performance is obviously improved. The reduced graphene oxide in the structural electrode is doped with nitrogen to enhance the electrostatic force between ions and the surface of the electrode, so that the electrochemical stability of the surface of the electrode is improved, and self-discharge is effectively inhibited. By improving an electrolyte and a structural electrode material, a cement-based supercapacitor with specific capacitance and mechanical properties is obtained and is combined with a building structure to realize building energy storage.

The invention discloses an office building energy consumption prediction method and system, and relates to the technical field of energy consumption prediction, and the method comprises the steps: collecting the current office building energy consumption data; decomposing the load sequence into a plurality of modal components, calculating the sample entropy of each modal component, and recombining the modal components by using a K-means clustering algorithm according to the calculation result of the sample entropy; taking each mode component after recombination as a node, taking the time sequence similarity between the mode components as an edge between the nodes, and constructing a graph structure; inputting the graph structure into a GCN-Transform model, extracting spatial features of nodes in the graph structure, and introducing a self-attention mechanism to extract time sequence features; inputting the spatial-temporal characteristics into a full-connection layer to obtain an energy consumption predicted value in a future period of time; according to the method, space cooperation and time dependence can be considered at the same time in a complex and changeable energy consumption scene, so that a more accurate prediction result is provided.

The invention discloses a distributed robust optimization scheduling method and system for a solar photovoltaic photo-thermal comprehensive utilization system, and belongs to the field of comprehensive utilization of renewable energy sources. The method comprises the steps of obtaining an electrical load and a thermal load based on an integrated load prediction model; obtaining typical sunlight photovoltaic and photo-thermal based on scene clustering analysis; based on photovoltaic, photo-thermal, electrical load and thermal load, an objective function and a constraint condition for optimal scheduling are set for the solar photovoltaic photo-thermal comprehensive utilization system, and based on the objective function and the constraint condition, a two-stage distribution robust optimal scheduling model is obtained by combining a comprehensive demand response mechanism; and solving the two-stage distribution robust optimization scheduling model by adopting a column and constraint generation algorithm to obtain a two-stage distribution robust optimization scheduling result. The method obviously improves the utilization rate of renewable energy sources, reduces the operation cost and carbon emission of the system, and is suitable for building energy systems in solar energy-enriched areas.

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.

The invention relates to an intelligent management and control method for a building energy-saving wall and a lattice structure wall applying the method, and solves the problems that although a traditional heat insulation material can reduce heat conduction, the function is single, and heat cannot be actively adjusted. Setting weights for the input data, and calculating the sum of the input data by adopting a weighted average method; according to a mapping relation between an interval range in which the sum of the input data falls and a grade, analyzing and determining the grade corresponding to the thermal working condition; according to the mapping relation between the thermal working condition grade and the overall regulation and control scheme, the overall regulation and control scheme is analyzed and determined, the determined overall regulation and control scheme is executed, and the overall regulation and control scheme comprises a phase change material regulation and control scheme, a lattice structure regulation and control scheme and a heat exchange fluid regulation and control scheme. The method has the following effects that the heat condition of the building is accurately regulated and controlled, the heat performance of the wall is improved, and efficient energy saving and intelligent management are achieved.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

The invention provides a building energy consumption real-time monitoring and carbon emission evaluation method based on digital twinning, and the method comprises the steps: obtaining regional energy supply real-time data and building group load demand data, predicting an energy supply fluctuation trend and a load change trend, and obtaining a short-term supply and demand dynamic prediction result; adjusting the operation mode of the building group combined cooling heating and power system in real time according to the mode switching optimization scheme, dynamically matching the output change of the distributed energy, and feeding back the output change to the digital twinborn model; carbon emission data is extracted from the optimized load distribution scheme, a digital twinborn model is used for predicting the total carbon emission amount and peak value change trend, the threshold value and constraint of carbon emission control are adjusted, and a final energy scheduling scheme is generated; and obtaining operation data according to the final energy scheduling scheme, calculating the energy utilization efficiency and the carbon emission reduction amount of the building group through an energy efficiency evaluation method preset in the digital twin model, and obtaining an execution effect evaluation result of the scheduling scheme.

The invention discloses a smart building energy consumption data monitoring management method and system, and relates to the technical field of smart building and energy management, and the method comprises the steps: constructing a dynamic graph structure, extracting joint features through employing a graph attention mechanism, weighting the Mahalanobis distance of a node through employing a batch average attention coefficient, and obtaining an abnormal scene feature vector; a target classification function is defined, an EPC-PSO algorithm is used for optimization, and a Softmax classifier is used for classifying abnormal scene feature vectors; defining an energy consumption efficiency objective function, decomposing into a sub-problem of each device by using Lagrange, outputting a global initial strategy vector by using a gradient descent method, defining a smart building task, constructing a matrix of a comprehensive benefit weight, converting the device and the task into a bipartite graph problem, and solving by using a Hungary KM algorithm; the batch average attention coefficient weights the mahalanobis distance, the robustness of anomaly detection is enhanced, and the comprehensive benefit of resources is improved by using Lagrange decomposition, a gradient descent method and a Hungary KM algorithm.

The invention relates to the technical field of green building energy consumption, and discloses a green building energy consumption real-time monitoring and intelligent adjusting system based on the Internet of Things, and the system comprises an Internet of Things multi-source sensing module which integrates a building internal and external environment sensing unit and an equipment operation state collection unit, and generates a comprehensive energy consumption characteristic signal through a heterogeneous data fusion gateway; the dual-channel decision module receives the signal, the load prediction unit outputs a building cooling and heating load prediction signal and a regional energy efficiency imbalance risk signal, the energy efficiency optimization unit generates an optimal equipment adjustment strategy signal, and the dynamic adjustment unit outputs a real-time energy consumption safety threshold; the intelligent execution control module and the self-adaptive equipment regulation and control unit adjust heating and ventilation equipment parameters, the multi-equipment collaborative management unit optimizes the running state of the lighting and air conditioning equipment group and collects feedback signals, and the man-machine interaction early warning platform generates a comprehensive energy efficiency risk index. According to the system, real-time monitoring and intelligent adjustment of energy consumption of the green building are realized, and refinement and collaboration of energy consumption management are improved.