1599 results about "Load forecasting" patented technology

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.

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 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.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention belongs to the technical field of office park electric load prediction, and relates to an office park electric load prediction and scheduling method for a novel electric power system. The method comprises the steps that S1, multi-source data fusion and panoramic view construction are carried out, historical load data of a park are collected, and park operation data, meteorological data and a special event calendar are obtained; s2, data preprocessing and feature directional extraction; s3, building and cooperating short-term, medium-term and long-term prediction models on the basis of multi-time scale prediction of model cooperation to form a hierarchical prediction system; s4, optimizing and evaluating a prediction result, and performing uncertainty evaluation on the prediction result in the step S3; and S5, generating and executing a hierarchical scheduling instruction based on hierarchical scheduling decision and execution of multi-objective optimization. According to the method, the problem of full-chain link splitting is solved, panoramic data view, collaborative prediction model, quantitative risk assessment and multi-target optimization scheduling are realized, prediction accuracy and scheduling flexibility are improved, and active participation in energy management of the office park is supported.

The invention discloses a multi-energy-storage thermal power generating unit load prediction and optimal scheduling method and system, and relates to the technical field of multi-energy-storage thermal power generating units, and the method comprises the steps: collecting the operation parameters and external environment parameters of a thermal power generating unit in real time, and constructing a real-time state parameter matrix; constructing a load prediction model based on the historical state parameter matrix, importing the real-time state parameter matrix into the load prediction model, outputting a load trend prediction curve, and triggering an early warning signal through a secondary discrimination mechanism; identifying a load disturbance value based on the load trend prediction curve, obtaining a load disturbance sequence, and decoupling the load disturbance sequence into a plurality of components; inputting the plurality of vectors into a preset decision network, dynamically correcting a constraint condition built in the decision network in combination with the early warning signal, introducing an improved dragonfly algorithm for optimization iteration, and generating an optimization scheduling instruction; according to the method, the adaptability of optimal scheduling and high-precision prediction of the load trend are improved.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention belongs to the technical field of load prediction in a low-voltage distribution area, and particularly relates to a time sequence prediction method for attention mixed multi-scale decomposition, which comprises the following steps: S1, preprocessing original time sequence data to obtain a standardized sequence X; s2, inputting X into MJDA, and outputting uniform high-dimensional representation U after feature enhancement; s3, inputting U into TCDA, and carrying out cross-dimension dependence modeling and deep nonlinear transformation to obtain a final enhanced feature U1; s4, inputting U1 output by the TCDA into a hybrid expert predictor group; the predictor group is composed of K parallel expert predictors, and a corresponding expert prediction result is obtained; meanwhile, U output by the MJDA is processed through a noise perception gating network, and weight distribution U used for expert predictor fusion is generated; and according to the U, carrying out weighted summation on the output of the K expert predictors to obtain a prediction result. According to the method, high-precision and high-stability load prediction can be realized in a low-voltage distribution area environment with limited resources.

The invention discloses a short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

Disclosed in the present invention are a load-prediction-based control method and apparatus for an energy storage apparatus, and a terminal device and a storage medium. The method comprises: using a preset load prediction model to perform prediction on the basis of real-time operation data, so as to obtain a predicted load value of a micro-grid within a future time period; then, on the basis of a power generation capacity and the predicted load value, determining whether there is surplus electricity after the current power generation capacity of the micro-grid satisfies a future load demand; if so, controlling an energy storage apparatus to be charged, so as to avoid the waste of electric energy; and if not, controlling the energy storage apparatus to supply power to the micro-grid, so as to ensure that the micro-grid can satisfy the future load demand, thereby effectively ensuring the stable power supply of the micro-grid.

The invention relates to the technical field of machine learning, and discloses a load prediction method based on dual feature processing and error correction. The method comprises the following steps: acquiring multi-source time sequence data; decomposing the historical load data into a plurality of modal components by adopting a variational modal decomposition algorithm; classifying each modal component into different frequency levels according to the size of the sample entropy; performing phase-space reconstruction according to the modal component of each frequency level and the corresponding external influence factor data, and generating a multivariable phase-space data set of each frequency level; respectively inputting the multivariable phase space data set of each frequency level into the corresponding load prediction sub-model, generating prediction output results, and superposing the prediction output results; constructing a residual sequence based on the historical load data and the initial load prediction result; inputting the residual error sequence into a residual error prediction model to obtain a load residual error prediction value; and compensating the initial load prediction result through the load residual prediction value. According to the scheme, the load prediction accuracy can be improved.

The invention relates to the field of heating ventilation air conditioner automatic control, and discloses an efficient central air conditioner cold station optimization control system and method based on physical AI. The system comprises a data acquisition module, an energy efficiency modeling module, a load prediction module, a rolling optimization module, an execution control module and a feedback correction module. The data acquisition module forms a time sequence data set; the energy efficiency modeling module adopts transfer learning to obtain a system energy efficiency model of a target domain cold station; the load prediction module outputs a predicted cold load sequence; the rolling optimization module is used for solving an optimal control sequence under the constraints of cooling capacity balance, an equipment operation boundary and a temperature difference threshold by taking a predicted cooling load sequence, a system energy efficiency model and a current equipment operation state as input under a model prediction control framework; the execution control module issues a first control action to control the water supply temperature, the water pump and fan frequency and unit start and stop; and the feedback correction module calculates a residual error and is used for closed-loop correction of the next period.

The invention relates to the technical field of building intelligent control and energy saving, and discloses a building equipment intelligent control optimization management system based on a green low-carbon building. The system comprises five subsystems, wherein an environmental parameter sensing subsystem is used for acquiring temperature and humidity gradients, illumination intensity distribution and personnel activity thermodynamic diagram data inside and outside a building through a distributed sensing network to form a multi-dimensional environmental state vector; the equipment operation state acquisition subsystem acquires an operation power curve, an energy efficiency conversion rate curve and an equipment health degree index of heating ventilation air conditioning, illumination and renewable energy equipment in real time; the dynamic load prediction subsystem establishes an energy consumption load space-time distribution prediction model according to historical data time sequence relevance; the control strategy generation subsystem is used for generating an optimized instruction set containing an equipment start-stop time sequence, a power regulation gradient and an energy distribution weight in combination with the current environment vector and a predicted load; the execution terminal adapts the subsystem to convert the instruction into a device compatible signal and calibrate a response delay.

A charging pile group dynamic power distribution method and system based on load prediction relates to the field of electric digital data processing, and the method comprises the following steps: obtaining charging data of charging piles in real time, and when a new vehicle requests to be charged, calculating the difference value between the required power and the current available power to obtain a power gap; and if the power gap exists, calculating a power amount which can be reduced by each charging vehicle according to the charging curve parameters and a preset rule. And calculating the charging time extension amount of each vehicle after power reduction, and calculating the pile position occupation time sensitivity in combination with the original predicted completion time. And sorting the sensitivities from small to large to form a power recovery candidate queue. And selecting proper vehicles from the candidate queue to form a recovery scheme, calculating the total pile position occupation time of the system corresponding to the scheme, and selecting the scheme with the minimum total time as the optimal scheme. And reducing the charging power of the selected vehicle according to the optimal scheme, and distributing the recovered power to the new vehicle. By implementing the method, the pile position utilization rate can be increased.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention relates to the technical field of power load prediction, in particular to a long-term power system load prediction method and system based on multi-scale decomposition fusion. The method comprises the steps of performing data preprocessing based on time sequence data; performing multi-scale decomposition and feature embedding on the preprocessed data to obtain a multi-scale load feature vector set; performing gating adaptive filtering and attention double-path fusion under the multi-scale load characteristics based on the multi-scale load characteristic vector set; performing independent prediction and prediction fusion on a fusion result based on a space-time attention gating mechanism; and evaluating a result after prediction fusion. According to the multi-scale prediction result space-time attention fusion mechanism provided by the invention, prediction information on different scales can be adaptively integrated, deviation caused by a single scale is avoided, the comprehensive performance of long-term prediction is further improved, and the method is suitable for various power system planning and operation scenes.

A green ammonia production hydrogen load prediction method based on sparse attention variational Bayes comprises the following steps: acquiring historical data of a green ammonia electrolysis hydrogen production industry through an acquisition sensor, and dividing the historical data into a training set, a verification set and a test set; performing standardization processing on the training set, and constructing an SVAE soft measurement training model; determining a final objective function suitable for SVAE; and performing real-time online prediction on the output target variable by using the current input characteristic variable by adopting the trained SVAE. The SVAE combines CNNs, PSSAM and a cross attention mechanism, and captures a long-term dependency relationship and a complex mutual relationship in industrial data. An optimized objective function is formulated in combination with variational reasoning and a Monte Carlo method and is used for offline training. The SVAE has remarkable advantages in the process of accurately predicting the hydrogen flow and optimizing the production process of the green ammonia, the production efficiency is improved, and the energy consumption is reduced.

The invention relates to the field of energy dispatching, and particularly provides an intelligent energy station dispatching method and device based on artificial intelligence, a terminal and a medium, and the method comprises the steps: obtaining multi-source data of an energy station, and generating a short-term load prediction result and a medium-and-long-term load prediction result; a multi-objective optimization function is constructed with the minimum energy consumption, the minimum operation cost and the minimum carbon emission, a Pareto optimal solution set is generated through an NSGA-II algorithm, a final operation plan is selected as a scheduling scheme according to the real-time electricity price and carbon price weight, the scheduling scheme is disassembled into executable instructions, and the executable instructions are issued to a field control unit after safety certification; making a maintenance plan according to the medium-term load prediction result, and making expansion and reconstruction according to the long-term load prediction result; and continuously training and optimizing each model. According to the method, multi-source data is utilized, multi-time-scale accurate prediction is realized, multi-target collaborative optimization is carried out, and a scheduling plan is ensured to be safe and reliable.

The invention belongs to the technical field of power load prediction and artificial intelligence, and provides a microgrid short-term load prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of historical operation data, and obtaining training data; embedding the electric power feature as a physical prior feature vector into an LSTM neural network to obtain a physical embedded vector, and fusing the training data with the physical embedded vector; pre-training the fusion sequence by using an LSTM neural network to obtain a first physical constraint LSTM model; a Wasserstein GAN is used to generate a confrontation framework for joint training, and a second physical constraint LSTM model is obtained; freezing a discriminator of the second physical constraint LSTM model and performing joint training to obtain a micro-grid short-term load prediction model and predict the real-time operation data to obtain a short-term load prediction result. The short-term load prediction accuracy of the micro-grid can be improved.

The invention provides a low-voltage power distribution network load change trend intelligent prediction and management method, belongs to the field of low-voltage power distribution network load prediction, and is used for solving the problems of large load fluctuation, insufficient prediction precision and high cloud deployment delay of a hybrid industry transformer area in related technologies. The method is deployed at an edge node of a transformer area, high-quality data is output through multi-modal data anomaly detection and scene completion, a four-dimensional dynamic load portrait is constructed based on the high-quality data, model super-parameter self-adaptive parameter adjustment is realized by combining transfer learning and Bayesian optimization, and accurate load data is output through three-dimensional linkage resource scheduling and dynamic fusion residual error correction. The method improves the load prediction precision and efficiency, reduces the response delay, and can effectively support the real-time scheduling of the power distribution network.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

The invention discloses an energy storage system intelligent energy management control method and system, and relates to the technical field of energy storage system intelligent energy management control, and the method comprises the steps: obtaining an energy storage unit operation state parameter and an environment disturbance factor matrix through a multi-source data collection device, and carrying out the data preprocessing; and constructing a long-short-term memory neural network model, dynamically adjusting a prediction time window of the model and optimizing a target function weight coefficient based on the load predicted by the model, and determining a charging and discharging control instruction. And updating and optimizing the target function weight coefficient and the charging and discharging strategy library through a reinforcement learning algorithm to realize self-adaptive optimization adjustment of the charging and discharging strategy. According to the method, high-precision energy management and intelligent optimization control of the energy storage system in a complex environment are realized. The load prediction accuracy and the energy utilization rate of the system are improved, the aging rate of the battery is reduced, the service life of the battery is prolonged, the adjusting capacity of the energy storage system is improved, and the overall stability of the energy storage system in dynamic change is enhanced.

The invention relates to the field of power load prediction, and particularly provides a multi-model dynamic fusion load prediction method and system, a terminal and a medium, and the method comprises the steps: constructing a plurality of different types of load prediction models, carrying out the independent training of each load prediction model through a training set, and carrying out the verification of a verification set on a verification set; calculating a dynamic weight corresponding to each load prediction model by adopting a Monte Carlo algorithm on the basis of similar day data similar to the prediction target day in the test set in feature; acquiring historical load data in a preset time period before the current moment, and meteorological data and time characteristic data at the corresponding moment to form a model input data set; preprocessing the input data set, and inputting the preprocessed input data set into each trained load prediction model to obtain an initial load prediction value corresponding to each load prediction model; and according to the dynamic weight, performing weighted fusion on each initial load prediction value, and outputting a final load prediction value. The accuracy of load prediction is improved.

The invention discloses a household energy storage dynamic optimization method and system combined with load feature learning, which are used for reducing prediction errors caused by sudden loads and improving response efficiency and economical efficiency of a household energy storage system. The method comprises the following steps: calculating a prediction error between real-time total load data and a basic prediction result, identifying and locking one or more high-power electric appliances causing the prediction error as target electric appliances, and decomposing and extracting historical operation state data of the target electric appliances from the total load data; constructing a time sequence feature vector based on the historical operation state data, and training by using a recurrent neural network model to extract the sudden load feature of the target electric appliance; fusing the optimized sudden load characteristics with a basic prediction result to generate a total load prediction curve; and based on the total load prediction curve, combining the residual capacity and the operation health state of the household energy storage system, and adopting a multi-objective optimization method to dynamically generate a charging and discharging strategy.

The invention relates to a centralized heating balanced regulation and control method, device and equipment and a medium. The method comprises the steps that firstly, the heat supply area of a user and the heat use state of a neighbor are collected, the basic flow of the jet device is calculated according to an industrial standard formula, and final flow containing resistance characteristic data is obtained through secondary correction by combining a neighbor heat use state correction coefficient and an environment temperature influence factor. Extracting flow and inlet and outlet pressure difference from jet device parameters, adjusting a valve to enable the resistance proportion to reach a preset interval, calculating parameters such as unit area heat consumption by combining real-time flow and supply and return water temperature difference, generating regulation and control basic data through coupling analysis, adjusting the jet device when the regulation and control basic data reach the standard, and generating a dynamic scheme by combining a thermal load prediction model. And finally, capturing the flow pulse signal according to a fixed period to calculate a difference value, generating a valve control signal according to a threshold value, acquiring the water supply and return temperature after execution, and coupling with a dynamic scheme to generate a regulation feedback signal. By adopting the method, accurate regulation and control can be realized through multi-dimensional analysis, and the flow distribution accuracy and the heat supply balance are improved.

The invention relates to a multi-scene electric quantity load prediction method and system based on a hybrid expert model. The method comprises the following steps: collecting multi-scene historical electric quantity load data and constructing a historical electric quantity load sequence of each scene; constructing an expert model library; constructing global expert embedding, calculating the dependency degree of each model in the expert model library in different scenes based on the global expert embedding, and constructing an expert dependency matrix; a hybrid expert model is constructed based on the expert model library, and for any scene, a gating network of the hybrid expert model calculates a model weight distribution vector for the scene based on a historical power load sequence of the scene and an expert dependency matrix; and inputting the historical electric quantity load sequence of the scene into each model in the hybrid expert model to obtain an initial electric quantity prediction result of each model, and performing weighted summation on the initial electric quantity prediction results of all the models based on the model weight distribution vector to obtain an electric quantity load prediction result in the scene.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention provides a multi-mode hybrid expert model electricity transaction data processing method and system, a storage medium and electronic equipment. The method comprises the steps of collecting multi-source heterogeneous data in an electricity market environment; extracting multi-modal features of the multi-source heterogeneous data, and performing dynamic weighted fusion to obtain fusion features, wherein the multi-modal features comprise time sequence features, meteorological features, image features, text features and market features; different expert models including a load prediction model, a price prediction model, a risk assessment model and a strategy optimization model are allocated for processing and analysis results are obtained according to the fusion features; and generating output data in combination with the analysis result, wherein the output data comprises a service instruction and an analysis report. According to the method, the power transaction prediction precision is remarkably improved, the reasoning delay is greatly reduced, the dynamic adaptive capacity and a closed-loop self-evolution mechanism are achieved, the decision interpretability and the system robustness are enhanced, and the strict requirements of the power market for high precision, real-time performance and stability can be met.

The invention relates to a power load spatio-temporal dynamic knowledge graph construction and load prediction method, which comprises the following steps of: constructing a text and digital sequence hybrid vector coding module, providing a hierarchical entity relationship joint extraction framework oriented to power system load data, constructing a Multi-Encoder-Bi-GRU-CRF power load entity recognition model, and constructing a power load entity model. Constructing a power load spatio-temporal dynamic knowledge graph in combination with a predefined relation rule base; meanwhile, time-space sub-graphs are divided, a space-time coupling self-adaptive adjacency matrix is constructed, and the space-time dependency relationship between nodes is quantified; and finally, combining the knowledge graph node embedded vector and the adjacency relation embedded vector, and jointly extracting the spatial feature and the time feature of the power load by adopting a space-time diagram convolutional neural network. Therefore, the load prediction algorithm provided by the invention not only can give full play to the advantages of multi-modal semantic integration and space-time modeling capability of the knowledge graph, but also can improve the load prediction precision, assist in realizing refined energy management of the power system and assist in making an optimal scheduling strategy, and has a good engineering application prospect.

The invention provides a distribution network load early warning method and system based on multi-source data, and the method comprises the steps: carrying out the capacity grouping of the load of a coal-to-electricity user based on a daily load curve of the coal-to-electricity user in a historical time period, and obtaining a 96-point load coefficient of each distribution network region in a power distribution network; determining the capacity equivalent load of the coal-to-power user in the coal-to-power distribution network according to all 96-point load coefficients; matching the historical coal-to-electricity load data in the same period through the temperature information and the date label in the next power distribution period to obtain a load characteristic matrix of the coal-to-electricity user, and performing confidence correction on the load error of the next power distribution period through the load characteristic matrix to obtain a confidence correction value of the load of the coal-to-electricity user; and carrying out power flow aggregation on the capacity equivalent load according to the confidence correction value, and outputting a 96-point load prediction result of the coal-to-power distribution network in the next power distribution period. Based on the scheme, the temperature compensation prediction of the 96-point load of the distribution network in the coal-to-electricity distribution network can be realized.