3065 results about "Load forecasting" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

The invention relates to the technical field of power systems, provides an association rule analysis-based power load prediction method and system, and aims to solve the problem of hidden fault response lag caused by lack of an equipment health state and load fluctuation dynamic coupling mechanism in the prior art. And the problem of load prediction compensation deviation caused by insufficient weight quantization of the fault propagation path is solved. The method comprises the following steps: generating equipment state data according to a vibration spectrum and an insulation aging index of power equipment; performing fusion analysis, generating an equipment health degree evaluation index, and establishing a dynamic influence model of the equipment abnormal event on the power grid load fluctuation according to the association rule; identifying potential abnormal equipment, performing logic mapping, and generating a fault propagation path weight; and dynamically adjusting according to the weight to obtain an adjusted power load predicted value. According to the technical scheme provided by the invention, equipment vibration and insulation aging data are fused, a health assessment and fault propagation model is constructed, and load prediction is dynamically corrected to prevent and control power grid risks.

The invention relates to the technical field of smart power grids, and discloses a smart power grid power dispatching optimization method, which comprises the following steps of: firstly, acquiring power grid operation data, and processing data missing and noise problems by utilizing federal learning; and constructing a load prediction model through a dynamic time warping algorithm and a specific network. A multi-energy coupling scheduling model and a demand response game model are constructed, and a multi-time scale rolling optimization framework is established. And carrying out sensitivity analysis on scheduling parameters, designing a hierarchical collaborative optimization mechanism, and constructing a robust optimization model to cope with the power flow uncertainty. And integrating a scheduling instruction verification module, and deploying an online incremental learning mechanism. The method can effectively process data, accurately predict load, optimize multi-energy scheduling, guide demand response, deal with uncertainty, verify scheduling instructions and update the model in real time, improves the safety, reliability and economy of smart grid power scheduling, and realizes optimal configuration of power resources.

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

The invention discloses a power grid dispatching method and system adapting to power system requirements, and relates to the field of industrial big data, and the method comprises the following operation steps: S1, multi-source heterogeneous data collection and edge preprocessing; s2, knowledge graph construction and data fusion; s3, load prediction and renewable energy output prediction based on deep learning; s4, generating a dynamic optimization scheduling strategy; s5, carrying out security and credible execution on the data endowed by the block chain; and S6, real-time monitoring and closed-loop feedback optimization are carried out. According to the power grid scheduling method and system adapting to the power system demand, the scheduling method integrates edge calculation, block chain, deep learning and reinforcement learning, can realize multi-source data real-time processing, dynamic optimization strategy generation and data security and credibility, improves the power grid operation efficiency, stability and renewable energy consumption capability, and improves the power grid scheduling efficiency. And the dynamically optimized scheduling strategy can reduce the operation cost of the power grid, and can reduce carbon emission at the same time.

The embodiment of the invention discloses a park load prediction method and device based on multi-modal data and a medium, and relates to the technical field of micro-grids, and the method comprises the steps: collecting multi-modal micro-grid data through a multi-source heterogeneous sensor network disposed at a power distribution node, the multi-mode micro-grid data comprises electrical measurement data, environment monitoring data and equipment state data; constructing a dynamic space-time incidence matrix according to the multi-modal micro-grid data, and performing feature extraction based on the dynamic space-time incidence matrix through a space-time diagram convolutional network deployed at an edge calculation node to determine a space-time feature vector; and inputting the spatio-temporal feature vector into a time sequence fusion prediction model, outputting a load prediction result, and generating an optical storage cooperative scheduling instruction according to the load prediction result, the load prediction result including a load prediction value and a confidence interval.

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

The invention relates to the technical field of power distribution network operation management, and discloses a power distribution network capacity-increase-free access system based on dynamic coordination. The system comprises a multi-source data acquisition and fusion unit which acquires multi-source data and fuses the multi-source data to generate dynamic power grid state characteristics; the multi-time-scale collaborative optimization architecture unit is used for constructing a layered architecture to realize optimization of different time scales; the space-time load prediction unit is used for predicting load distribution based on the space-time diagram model; and the multi-target dynamic optimization solving unit is used for constructing a model to solve an optimal scheduling instruction. In addition, a layered communication protocol unit is arranged to guarantee communication, and a voltage stability cooperative control unit and a distributed energy storage cooperative control unit are arranged to improve the stability of the power grid. Through cooperation of multiple technologies, capacity-increase-free access of the power distribution network is realized, the operation efficiency is improved, the loss is reduced, the voltage stability is ensured, and challenges brought by distributed energy and electric vehicle charging are effectively dealt with.

The invention discloses a hierarchical collaborative management method and system for a virtual power plant based on multi-modal deep learning, and the method comprises the steps: constructing a four-dimensional data collection system, and achieving privacy enhancement preprocessing through federated learning and a differential privacy technology; a Bi-LSTM and a heterogeneous graph neural network are adopted to construct a three-mode deep fusion model, the weight is dynamically adjusted in combination with an environment-user dual-drive attention mechanism, and the load prediction precision and the space resource utilization rate are improved; a multi-target scheduling strategy is generated based on a five-dimensional target function and an improved DDPG algorithm, and physical feasibility is ensured through digital twinborn pre-verification; efficient execution and excitation transparency are realized through edge layer FPGA + NPU hardware acceleration and block chain evidence storage; and constructing a user participation ecology by using a natural language interaction strategy engine and a stepped incentive mechanism. The power grid economy, the equipment reliability and the user participation degree are remarkably improved, and intelligent upgrading of the virtual power plant is promoted.

The invention belongs to the technical field of power load prediction, and particularly relates to a Mama-based endogenous and endogenous variable fusion power load prediction method, which comprises the following steps of: collecting and preprocessing a data set; embedding an endogenous variable, dividing a power load endogenous sequence into N non-overlapping Patch blocks, adding position codes to each block, generating a block token Pen of block fine granularity through linear projection and position codes, and introducing a learnable global token Gen to represent the macroscopic state of the sequence; global embedding of exogenous variables; performing cross-granularity fusion on internally and externally generated tokens; carrying out Mama time sequence dependence modeling; and performing multi-step prediction output, mapping the time sequence characteristics into future multi-step load prediction values through a full connection layer, restoring the original data scale after reverse normalization, and outputting a final prediction result. According to the method, through a block embedding strategy and a dynamic parameter selection mechanism, the adaptability of the model to a complex power scene is remarkably improved, and reliable technical support is provided for efficient scheduling and marketization operation of an intelligent power grid.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

The invention provides a multi-level geothermal well collaborative scheduling method and system based on multi-time scale prediction, and relates to the technical field of data processing. The method comprises the following steps: constructing a well group output model based on operation parameters of a shallow geothermal well group and a deep geothermal well group; constructing a multi-scale load prediction model based on the historical load data and the historical meteorological data; determining a load prediction result of the target time period by using the multi-scale load prediction model, wherein the load prediction result comprises a day-ahead load, a day-mid load and a real-time load; and performing joint calculation by using the well group output model, the load prediction result and the multi-objective optimization function to obtain optimal scheduling parameters of the shallow geothermal well group and the deep geothermal well group in the current scheduling period. According to the technical scheme, the output ratio of the shallow geothermal well and the deep geothermal well can be dynamically coordinated according to the predicted load requirements of different time scales, and efficient utilization of multi-level geothermal resources is achieved.

The invention relates to the technical field of air conditioner cooling load prediction, and discloses an air conditioner cooling load prediction method and system. The method comprises the following steps: acquiring temperature, humidity and CO2 data through an environment sensor network and carrying out standardization processing; establishing a time slice matrix based on the environmental parameters and the personnel density, and obtaining user behavior characteristics; constructing a wall heat storage model by combining the environmental parameters, and calculating instantaneous heat flow; establishing a spatial downscaling model according to the heat flow and meteorological data; time sequence features are extracted based on local weather and heat flow data, and the cooling load is predicted; and a time-phased refrigeration control instruction is generated accordingly. By establishing a data processing and analysis process, factors such as building microclimate characteristics, user behavior modes and wall dynamic thermal characteristics are comprehensively considered, and more accurate prediction of the cooling load of the air conditioner is realized.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention discloses a thermal power generating unit deep peak regulation control system, and relates to the technical field of thermal power generating unit deep peak regulation, the thermal power generating unit deep peak regulation control system comprises a deep peak regulation control center, and the deep peak regulation control center is in communication connection with a data acquisition module, a load prediction module, a working condition monitoring module, a distributed control module and a safety monitoring early warning module. By introducing a distributed control architecture and bat algorithm optimization, each subsystem of the thermal power generating unit is divided into a plurality of local control units, independent and cooperative control is realized, the flexibility and the response speed of the thermal power generating unit in the deep peak regulation process are remarkably improved, and the reliability of the thermal power generating unit is improved. Distributed control ensures that each subsystem can quickly adjust operation parameters according to actual load requirements, and the global optimization capability of the bat algorithm further improves the self-adaptability and decision-making efficiency of the system in a complex working condition, so that the problem of response lag in a traditional centralized control mode is effectively solved.

The invention provides a smart park Internet of Things distributed energy intelligent analysis and control method and device, and relates to the technical field of smart park energy management, and the technical scheme is characterized in that the method comprises the steps: obtaining energy consumption data, meteorological data, distributed energy power generation data and user behavior data of each region of a smart campus; generating a basic load prediction value and an event-driven load prediction value according to the acquired data; making a day-ahead energy scheduling plan; when a deviation exists between the actual energy consumption condition and the predicted value, adjusting the energy scheduling plan by adopting a rolling scheduling strategy; and executing the adjusted energy scheduling plan, and controlling the distributed energy system and the energy consumption equipment to optimize campus energy utilization. The smart park Internet of Things distributed energy intelligent analysis and management and control method and device provided by the invention have the advantages of accurately predicting complex and changeable energy consumption demands and utilizing the distributed energy to the maximum extent.

The invention discloses a micro-grid anti-countercurrent control method and system based on multi-objective optimization, and the method comprises the steps: generating a dynamic load prediction result according to the power grid side voltage, user side load power and energy storage system charge state data collected by an energy management system in real time; based on the dynamic load prediction result, obtaining a countercurrent risk quantitative index; generating a corresponding dynamic anti-reflux threshold curve according to the reflux risk quantitative index; the dynamic anti-reflux threshold curve is corrected through a real-time collaborative verification module, and an anti-disturbance optimization threshold is generated; and converting the anti-disturbance optimization threshold value into a PCS power regulation and control instruction, synchronously displaying a threshold value execution state and reverse feed early warning information on an energy management interface, and triggering adaptive power clamping protection when detecting that the voltage is out of limit or the power is reverse. According to the embodiment of the invention, the intelligent level of countercurrent prevention and control of the micro-grid can be improved, and safe and stable operation of the grid is guaranteed.

The invention discloses a multivariable fusion power load prediction method and system, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining first data, and synchronizing the first data into second data based on a physical constraint interpolation method; shielding the harmonic dominant frequency band based on the second data, and de-noising the load waveform by combining the real fluctuation of the filtering separation load; a load prediction model is constructed based on the denoised load waveform in combination with a harmonic distortion rate weighted double-flow network, prediction model parameters are corrected in real time, and a load prediction value is output; the real-time correction is a dynamic correction strategy based on wavelet transform. Through a dynamic selection interpolation method, the load data, the meteorological data and the new energy output data can be synchronized on a unified time scale, the synchronism and precision of different data sources can be ensured, high-quality input data is provided for a prediction model, and the reliability of a prediction result is improved.

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

The invention relates to the field of energy management, and discloses an industrial park load prediction method based on artificial intelligence, and the method comprises the steps: collecting historical load, production plan, weather and holiday and festival information through multi-source data; through data preprocessing, a time sequence feature extraction module fusing an attention mechanism and LSTM and a deep learning model integrating a sudden change adaptation module are constructed, and high-precision load prediction is realized. Wherein the abrupt change adaptation module dynamically adjusts model parameters to cope with load abrupt change through sliding window statistical feature monitoring, incremental learning and GAN simulation abrupt change scenes; and a real-time feedback mechanism further optimizes the prediction result, and generates a power dispatching suggestion in combination with a dynamic electricity price strategy. According to the method, the problems of large prediction deviation and poor adaptability of a traditional model in a load sudden change scene are solved, and the efficiency and stability of industrial park energy management are remarkably improved.

The invention discloses a rural power grid user power load prediction method and system based on a liquid neural network. The method comprises the following steps: firstly, collecting rural power grid user power load historical data including multi-dimensional features such as weather and agricultural modes, and carrying out data preprocessing; then constructing liquid neurons based on biological neuron dynamics, and modeling the state of the liquid neurons through a differential equation; thirdly, constructing a liquid neural network based on liquid neurons, improving the characterization capability of multi-scale time sequence data through multi-level time constant setting and time gating residual connection, and supplementing network initial information in combination with a multi-layer perceptron architecture; completing model training by using the time sequence data set; the actual application performance of the model is tested based on the test data and the actual application scene; and finally, deploying the model to practical application, and carrying out power load prediction on rural power grid users. According to the method, the expression capability of the model for the multi-scale time sequence data is improved, and high-precision rural power grid user power load prediction is realized.

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention relates to the technical field of power electronics, in particular to a control method for a low-voltage intelligent reactive compensation system of a submerged arc furnace. Comprising the following steps: acquiring three-phase voltage, current, power factor, harmonic content and temperature parameters of the low-voltage side of the submerged arc furnace through a sensor network; the load fluctuation of the submerged arc furnace in the future 1-5 minutes is predicted based on a deep learning algorithm, and a compensation demand pre-judgment value is generated; according to a load prediction result and a real-time working condition, performing dynamic switching from three modes of fixed compensation, grouped switching and continuous adjustment; harmonic component dynamic tracking compensation is carried out by combining an active power filter (APF) and a static var generator (SVG); the system overvoltage, overcurrent and temperature parameters are monitored in real time, and a grading protection mechanism is triggered and fed back to the compensation strategy adjusting module. The invention provides a control method for a low-voltage intelligent reactive compensation system of a submerged arc furnace so as to improve the response speed of the system, prolong the service life of equipment and guarantee the electric energy quality of a power grid.

The invention relates to the technical field of ship propulsion load prediction, in particular to a ship propulsion load prediction method, system and device under a high-fluctuation working condition and a medium, and the method comprises the steps: obtaining a ship operation parameter sequence under the high-fluctuation working condition; inputting the ship operation parameter sequence into a pre-trained propulsion load prediction model, and outputting a ship propulsion load prediction sequence of a specified time scale; the propulsion load prediction model comprises an improved time sequence convolutional network, a bidirectional long short-term memory network, an attention mechanism unit and a full connection layer. According to the application, bidirectional multi-scale features are extracted by improving a time sequence convolutional network, a bidirectional long-short-term memory network separates forward and backward time sequence processing paths to analyze a bidirectional dependency relationship, an attention mechanism unit adopts three-stage weight distribution to enhance key feature focusing, a full-connection layer implements nonlinear mapping, and a full-connection layer implements non-linear mapping. And collaborative optimization of the propulsion load prediction precision and the real-time response capability under the high-fluctuation working condition is realized.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.

The invention discloses an energy-saving control method and system based on a large refrigeration house, and the method comprises the steps: S1, obtaining cargo attribute information in real time through a cargo label, and inputting a dynamic load prediction model to generate a cooling capacity demand prediction signal in a future time period; s2, generating a multi-device cooperative control signal based on the cooling capacity demand prediction signal in the future period; s3, a shelf-level cooling capacity demand distribution signal is generated in combination with the cooling capacity demand prediction signal; s4, generating a directional cold airflow path signal matched with goods shelf distribution according to the goods shelf level cold capacity demand distribution signal; and S5, closed-loop feedback adjustment is conducted on the compressor frequency, the refrigerant flow and the air valve opening through an edge calculation module, and a dynamic balance control instruction of cooling capacity supply and space distribution is generated. The intelligent air quality monitoring method and system based on sensing data feedback can solve the problems of excessive refrigeration energy consumption waste caused by inaccurate cold capacity demand prediction of a large refrigeration house and extra energy loss caused by low cooperative efficiency of multiple devices.

The invention provides a heat supply load prediction and scheduling method based on energy storage peak regulation, and the method comprises the steps: obtaining multi-modal data of a heat supply system, carrying out the preprocessing of the multi-modal data, and constructing a space-time joint feature matrix based on the preprocessed multi-modal data; inputting the spatio-temporal joint feature matrix into a multi-modal depth load prediction model for heat supply load prediction to obtain a heat supply load prediction result; the multi-modal deep load prediction model is obtained by training a deep learning prediction model fused with physical constraints through system historical data; based on the heat supply load prediction result, a distributed energy storage scheduling strategy is generated in combination with a multi-agent reinforcement learning algorithm based on an MADDPG framework, and scheduling operation of the heat supply system is carried out according to the distributed energy storage scheduling strategy; and the energy utilization efficiency and the power grid stability are improved.

The invention relates to the technical field of air conditioning, in particular to a laboratory heating and ventilation load prediction and self-adaptive regulation and control method. According to the method, the infrared frame and the power sampling time mark are synchronized, the sensing flow is aligned and packaged, and the thermal diffusion evolution rate is generated. Lagging characteristics are determined in combination with power jump and temperature rise moments, and a heterogeneous dynamic coupling model is established. Extracting a physical evolution parameter as a mechanism operator, injecting the mechanism operator into a hidden layer of the prediction model, reconstructing a phase space, and calculating an air load increment in a lag window. And reverse mapping is executed based on the heat exchange characteristics to generate a feedforward instruction, and when the rate exceeds a threshold value, the weight is issued and dynamically corrected, so that closed-loop correction is completed. According to the method, deep coupling of feedforward prediction compensation and feedback residual adjustment is executed, and accurate regulation and control of the air volume and cooling and heating loads of the laboratory are achieved.