443results about How to "Reduce forecast error" patented technology

The invention relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention relates to a tidal reach tide level forecasting method and system based on a combined model. The method comprises the following steps: mining the average propagation time of upstream and downstream tide levels through a derivative dynamic time warping method; feature decomposition is conducted on the tide level multi-influence-factor data through set and empirical mode decomposition, and high-frequency component information and low-frequency component information are extracted; a combined model is constructed by combining a deep residual shrinkage network, a bidirectional long-short term memory network and an attention mechanism, and spatial-temporal features are mined. The tide level propagation time between upstream and downstream tide level stations is mined by using a derivative dynamic time warping method, and a more accurate time reference is provided for tide level forecasting; a set and empirical mode decomposition method is adopted to perform characteristic decomposition on tide level data, spatial characteristics and time characteristics are extracted through a combined model, and an attention mechanism is utilized to perform fusion, so that the mining and learning ability of the model on multi-scale nonlinear characteristics and non-stationary characteristics is improved, and the tide level forecasting precision is further improved. And the forecasting error in the storm surge period is reduced.

The invention discloses an ultra-short-term photovoltaic power prediction method and system based on sky-ground multi-modal data fusion. The method comprises the following steps: S1, acquiring multi-modal data; s2, processing and fusing the multi-modal data; s3, evaluating the quality of the fused multi-modal data, and judging whether the peak signal-to-signal ratio of the fused multi-modal data is greater than or equal to a preset threshold value or not: if so, entering the next step; s4, constructing an integrated prediction model, and performing iterative training on the integrated prediction model by using the multi-modal data; and S5, inputting the fused multi-modal data into the integrated prediction model to obtain a photovoltaic power prediction trajectory. According to the method, the complex change of the surrounding environment of the photovoltaic power station can be more comprehensively captured by fusing the sky-ground multi-mode data, so that the accuracy of photovoltaic power prediction is improved. By processing and fusing data from different sources, the advantages of various data can be fully utilized, and the comprehensiveness and depth of prediction are improved.

The invention discloses a method and a system for establishing a comprehensive load model containing a distributed power supply, and relates to the technical field of load model establishment, after the establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.

The invention discloses a hydrogen-containing energy storage micro-grid dispatching optimization method in combination with ARIMA-LSTM and DDPG. Comprising the following steps: firstly, acquiring historical renewable energy productivity data, historical power load data, historical electricity price and historical energy storage equipment state of the hydrogen-containing energy storage microgrid; secondly, historical renewable energy productivity and power load data are predicted through an ARIMA-LSTM model, a complete state space is constructed in combination with the renewable energy productivity, the electricity price, the energy storage state and the like of the current time slot and input into an Actor and Critic network of the DDPG, a DDPG network model is trained, and a reward function model in the DDPG network model aims at optimizing the annual operation cost; the constraints comprise equipment operation constraints, renewable energy source output constraints, power imbalance and the like; and finally, a micro-grid scheduling strategy responding to external environment change is obtained, dynamic scheduling optimization of each device is realized, and adaptability and robustness of the system are improved.

The invention relates to the technical field of seat cushion framework detection, in particular to an automobile seat cushion framework fatigue test method and system. The method comprises the following steps: acquiring information data of an automobile seat cushion framework sample; performing skeleton key part screening on the automobile cushion skeleton sample information data to obtain skeleton key part screening data; performing sensor data acquisition on the automobile seat cushion framework sample based on the framework key part screening data to obtain stress sensor acquisition data and deformation sensor acquisition data; and carrying out loading mode design on the information data of the automobile seat cushion framework sample to generate fatigue loading mode data of the automobile seat cushion framework. According to the invention, the accuracy and reliability of the fatigue test of the automobile seat cushion framework are improved by accurately screening key parts of the framework, designing a real loading mode, deeply detecting damage, predicting the service life and optimizing materials.

The invention provides an electroencephalogram signal recognition method of a domain decoupling electroencephalogram representation learning model based on hybrid experts, and the method comprises the steps: carrying out the spatial domain coding of sample electroencephalogram data, and obtaining spatial domain feature electroencephalogram data; performing frequency domain coding on the spatial domain feature electroencephalogram data to obtain sample electroencephalogram feature data; performing multi-head self-attention calculation on the sample electroencephalogram feature data to obtain sample electroencephalogram attention feature data; performing hybrid expert domain decoupling processing on the sample electroencephalogram attention feature data to obtain sample electroencephalogram reconstruction data; and according to the sample electroencephalogram reconstruction data and the sample electroencephalogram feature data, performing parameter adjustment on the electroencephalogram representation learning model to obtain a trained electroencephalogram representation learning model.

The invention discloses a distributed photovoltaic short-time output dynamic prediction method and system. The method comprises the following steps: acquiring micro-meteorological data, a cloud picture image and historical output data of a target area; calculating a building shadow shielding coefficient, and generating shadow influence factor time sequence data; obtaining cloud image occlusion index time series data based on the cloud image and a pre-constructed cloud image occlusion prediction model; fusing the data, and constructing and training a short-time output dynamic prediction model based on a long short-term memory network; predicting photovoltaic output by using real-time data and the trained model, and adjusting the model according to a prediction error and a confidence interval; according to the method provided by the invention, various factors such as microclimate, building shadow and cloud layer shielding are comprehensively considered, and the complex nonlinear relationship between the factors and the photovoltaic output is captured by using the deep learning model, so that the accuracy and robustness of distributed photovoltaic short-time output prediction are improved.

The invention belongs to the technical field of power systems, and particularly relates to a distribution network load prediction model based on artificial intelligence, which comprises a DM-LSTM neural network model, and a distribution network load prediction method of the DM-LSTM neural network model comprises the following steps: S1: firstly, collecting original data; comprise historical generating capacity, weather data, season information, geographic position parameters and equipment performance indexes; s2, establishing a Deep MLP deep network encoder, inputting the collected original data, and then carrying out initialization and normalization processing on data information; s3, a Sat iotempora l LSTM (Long Short Term Memory) model is built; s4, fusing the output of the Deep MLP deep network encoder and the output of the Sat iotempora l LSTM model to obtain a mixed weight so as to reduce a prediction difference; and S5, constructing a Softmax layer at the tail end of the Sat iotempora l LSTM (Long Short Term Memory) model. According to the method, static and dynamic factors are comprehensively considered, more background information, time sequence modeling capability and dynamic mode recognition capability are provided, prediction errors are effectively reduced, and prediction accuracy and reliability are improved.

The invention discloses a working face earth surface rock movement digital intelligent observation and through-structure safety intelligent prediction method, and belongs to the technical field of mining industry and information, and the method comprises the following steps: determining a structure movement deformation safety early warning value; moving deformation data are collected and preprocessed; analyzing a dynamic change rule of surface movement deformation by applying finite element analysis; building a structure movement deformation prediction model; the structure movement deformation prediction model is trained and verified, and the trained model is used for structure movement deformation prediction; comprehensively evaluating safety and stability; and correcting parameters of the prediction model and continuously evaluating until the structure is safely pushed and mined. The invention provides a novel way with great potential for accurately predicting the rock migration trend and guaranteeing the safety of important facilities such as structures.

The invention discloses a chloride ion diffusion coefficient prediction method based on a hybrid machine learning model, and the method comprises the steps: widely collecting actual data, carrying out the preprocessing, carrying out the selection of parameters affecting a chloride ion diffusion coefficient through an LASSO regression feature selection method, and preventing the overfitting problem of a regression model on a training set; carrying out regression analysis on a plurality of parameters of the chloride ion diffusion coefficient on the training set by utilizing a distributed gradient boosting algorithm (LightGBM) based on a decision tree algorithm, taking data obtained by carrying out dimension raising processing on an obtained predicted value and an original parameter as input of GPR (Gaussian process regression), taking a true value as output, training a GPR model, analyzing a GPR prediction result, and obtaining a prediction result of the chloride ion diffusion coefficient. And a light GBM-GPR model is constructed by using a Bayesian parameter optimization method. According to the method, the characteristic that the LightGBM prediction error is small is reserved, the uncertainty probability interval is given, and the prediction capacity of the chloride ion diffusion coefficient is improved.

The invention belongs to the field of emitters, and discloses a large aerospace component hot working quality and energy consumption cooperative control method and system, and the method comprises the steps: collecting data to form an equipment energy consumption feature data set; setting a plurality of groups of multi-pass process parameters, performing simulation calculation to obtain corresponding quality data, and forming a quality feature data set; respectively inputting the energy consumption characteristic data set and the quality characteristic data set into a to-be-trained adaptive fuzzy neural network for training until the trained model converges, and obtaining an energy consumption prediction model and a quality prediction model; respectively inputting the data into the two models, and predicting a standard value of energy consumption required by a processing component to obtain a quality prediction value of a hot processing component product; and the particle swarm optimization is used for adjusting and optimizing the process of the subsequent process, so that the cooperative control of the quality and energy consumption of the hot-working component is realized. By means of the method, flexible control over the whole forging process of the large aerospace component can be achieved, the product quality and energy consumption are stabilized, and the production benefits of factories are improved.

The invention provides a self-adaptive correction pharmaceutical process quality detection method and robot, and belongs to the technical field of medicine quality detection. The method comprises the following steps: acquiring hyperspectral image historical data of a drug, and carrying out data preprocessing; key spectral features in historical data of the hyperspectral image are extracted, and a multivariate correction model is constructed; collecting hyperspectral image real-time data of drugs, and forming and dynamically updating a data set; when the dynamic capacity of the data set reaches a set threshold value, the data set is used for real-time training of a multivariate correction model, and adaptive correction and real-time automatic updating of the multivariate correction model are carried out by dynamically adjusting parameters. The robot comprises a data acquisition module and a quality detection module which are connected with each other; the data acquisition module is integrated with hyperspectral imaging equipment; and the quality detection module carries a multivariate correction model. Through continuous learning, a multivariate correction model is automatically updated in real time to adapt to dynamic changes in the pharmaceutical process, and the production efficiency and the quality control precision are improved.

The invention discloses a blue-green algae growth prediction method and an intelligent regulation and control device, and relates to the field of blue-green algae growth prediction and regulation and control, and the method comprises the steps: carrying out the collection of blue-green algae growth related data, data preprocessing, random forest model construction, SVM model construction, and model weight fusion of a final random forest prediction model and a final SVM prediction model to obtain a fusion model. And dynamically optimizing the fusion model, finally obtaining the dynamically optimized fusion model, obtaining a real-time prediction result according to the dynamically optimized fusion model by using the blue-green algae growth related data collected by the sensor module in real time, and adjusting the blue-green algae growth culture conditions in real time. According to the method, the advantages of the random forest model and the SVM model are combined, a fusion model with dynamic optimization and real-time adjustment functions is developed, and the accuracy and adaptability of blue-green algae growth prediction are remarkably improved. And meanwhile, data acquisition, processing and prediction modules are integrated, so that the accuracy of blue-green algae growth prediction and the automation degree of the system are remarkably improved.

The invention discloses an atmospheric density prediction method, device and equipment based on multi-model fusion. Comprises: obtaining target training data; training a first meta-model by using the target training data, so that the first meta-model allocates corresponding M first weight values to the M predicted values of each prediction period based on the historical environment parameters corresponding to each prediction period; when a first loss function value of the first meta-model meets a condition, training of the first meta-model is completed, the first loss function value is a mean square error of a first predicted value corresponding to each prediction period in a plurality of prediction periods and an actually measured value, and the first predicted value is M first weight values distributed by using the first meta-model in each prediction period; the M prediction values corresponding to each prediction period are subjected to weighted summation; distributing M first weight values for the M target predicted values through a first meta-model; and performing weighted summation on the M target prediction values by using the M first weight values, and determining the atmospheric density of the target prediction period.

The invention belongs to the technical field of new energy prediction, and discloses a power market new energy joint prediction method and system based on SA-GAN, and the method comprises the steps: obtaining the historical data of a power market; respectively adding the attention mechanism into a generator network and a discriminator network in the generative adversarial network, and establishing an attention-generative adversarial prediction model; inputting historical data of the electricity market into the attention-generative adversarial prediction model for training, and continuously optimizing parameters of the attention-generative adversarial prediction model through adversarial learning of a generator and a discriminator; and performing joint prediction by using the trained and optimized attention-generative adversarial prediction model to obtain new energy future power generation data. According to the method, the limitation of a traditional method on complex and nonlinear data processing is effectively solved, joint prediction of multivariable data is realized, interaction among variables is fully considered, and a more comprehensive and accurate prediction result is generated.

The invention discloses a park comprehensive energy optimization scheduling method based on artificial intelligence, and relates to the technical field of energy scheduling, and the method comprises the steps: employing a Temporal Fusion Transform model to carry out the time series modeling of electric, thermal and cold loads and wind-solar power generation output in a park, capturing the load change trend, generating the probability distribution of prediction errors, and carrying out the prediction of the prediction errors; load characteristics and wind-solar power generation volatility in the park are dynamically adapted, and prediction errors are remarkably reduced; in addition, in combination with real-time monitoring and an event-driven mechanism, abnormity of a load or equipment state is rapidly responded, energy storage discharge is dynamically adjusted, standby equipment is started or a load plan is modified, and stable operation of the system is ensured. Real-time energy assessment and transaction further efficiently utilize complementary energy, and can quickly make up for the energy gap, thereby enhancing the flexibility and economy of park comprehensive energy management.

The invention relates to the field of vehicle emission prediction, and discloses a gasoline vehicle emission prediction method based on an XGBoost-SVR combined model, and the method comprises the following steps: S1, collecting the instantaneous emission data of pollutants of a gasoline vehicle and vehicle driving parameters, and carrying out the time sequence calibration processing; s2, singular spectrum analysis is adopted to decompose and reconstruct the pollutant emission data; s3, dividing the processed pollutant emission data and vehicle driving parameters into a training set, a verification set and a test set; s4, establishing an XGBoost model, and predicting the instantaneous discharge amount of pollutants by inputting vehicle driving parameters; s5, calculating a residual error between a predicted value and an actual value based on the verification set; and S6, correcting the residual error by using an SVR model to obtain a final pollutant emission prediction value. According to the method, the XGBoost and SVR models are combined, so that high-precision prediction of the pollutant emission of the gasoline car is realized, data noise is effectively removed, the experiment cost is reduced, and the generalization ability and adaptability of the model under complex working conditions are improved.

The invention discloses an extradosed cable-stayed bridge cable force optimization method, device and system and a storage medium. The method comprises the steps that S1, cable force and bending strain energy training samples of an extradosed cable-stayed bridge are obtained; s2, optimizing SVM parameters under the cable force and bending strain energy training samples of the extradosed cable-stayed bridge by adopting an IGWO algorithm; and S3, taking the cable force value after secondary tensioning as a to-be-optimized parameter, carrying out iterative optimization by using an IGWO algorithm, taking the cable force combination output after evolution of each generation as an input vector of an SVM for nonlinear prediction, outputting a population fitness value under the current cable force, and repeatedly reducing the time cost when the optimization model carries out fitness evaluation so as to improve the fitness evaluation accuracy. And IGWO-based combination optimization is realized. By the adoption of the technical scheme, the calculation efficiency and calculation precision of the finished cable force optimization of the extradosed cable-stayed bridge are improved.

The invention discloses an intelligent wind field forecasting method and an intelligent wind field forecasting system based on combined application of ST-LSTM (Long Short Term Memory) and Swin-Transform. The method belongs to the technical field of data processing, and comprises the following steps: constructing a time sequence prediction model based on ST-LSTM; inputting the historical data of the wind field into a time sequence prediction model based on ST-LSTM to obtain future wind field prediction data; the method comprises the following steps: constructing a wind field space downscaling model based on a Swin Transform architecture; and inputting the wind field forecast data into the wind field space downscaling model based on the Swin Transform architecture to obtain wind field refined forecast data. According to the method, the STM-LSTM is adopted as the extension of the LSTM, the long-term and short-term dependency relationship in time and space can be better captured when time sequence data is processed, and the Swindow-Transform can extract features in a global range through a self-attention mechanism of the Swindow-Transform, effectively capture the mutual influence between different positions in a wind field and further improve the accuracy of forecasting.

The invention relates to the technical field of power generation prediction data processing, and discloses a photovoltaic power station power generation prediction method based on multi-model fusion, which combines a photovoltaic physical model, a power generation statistical model and a power generation prediction model, not only considers a physical rule, but also combines statistical learning and machine learning to improve the stability and accuracy of prediction, and improves the prediction efficiency. And predicting the generating capacity of the photovoltaic power station by using environmental meteorological data, electrical parameters of the photovoltaic power station and historical power generation data. Accurate photovoltaic power generation power prediction is beneficial to better arrangement of power generation plans and resource scheduling of the power grid. The prediction precision and stability are improved through multi-model fusion, so that power grid dispatching is more accurate and efficient, and power grid instability factors caused by photovoltaic power generation fluctuation are reduced. Therefore, photovoltaic power generation can be connected to a power grid more stably, the power demand is met, and wide application and development of clean energy are promoted.

The invention discloses a cement paste strength prediction method based on an integration algorithm. The method comprises the following steps: collecting original cement paste data; performing feature extraction on the original cement paste data to obtain to-be-predicted cement paste data; and sequentially inputting the to-be-predicted cement paste data into a trained convolutional neural network model, an XGBoost model and a random forest model to obtain the compressive strength of the cement paste test block with the standard size, so as to predict the cement paste strength according to the compressive strength of the cement paste test block with the standard size. According to the cement paste strength prediction method based on the integration algorithm, the strength of the cement paste is predicted by using three models, and respective advantages, the powerful capability of CNN in image and signal processing, the high efficiency of XGBoost in structured data processing and the robustness of a random forest in classification and regression problems can be utilized, so that the strength of the cement paste is predicted. Therefore, the prediction accuracy and robustness are improved.

The invention belongs to the technical field of hydrological prediction, and discloses a parallel architecture runoff prediction method fusing characteristic decomposition and an error correction strategy, and the method comprises the steps: collecting a runoff time sequence of a target region, and carrying out the decomposition of the runoff time sequence through employing a VMD optimized through an SSA algorithm; screening a subsequence most related to runoff change as a runoff prediction factor; inputting the runoff prediction factors into a parallel CNN-GRU model to extract spatial-temporal characteristics, and optimizing parameters of the parallel CNN-GRU model by using an SSA algorithm; inputting a runoff prediction factor into the optimized parallel CNN-GRU model for preliminary prediction, and predicting an error value based on the optimized parallel CNN-GRU model; and superposing the prediction error value to the initial prediction value to correct the prediction deviation, and obtaining a final runoff prediction value. According to the method, by combining the parallel CNN-GRU architecture, the SSA optimization algorithm and the error correction mechanism, the precision of regional runoff prediction is remarkably improved.

The invention discloses a short temporary rainfall prediction method based on multi-modal fusion and dynamic weighting loss. Comprising the following steps: firstly, inputting historical radar and ground station data, and carrying out normalization and downsampling processing; respectively inputting radar and ground station meteorological element data into three layers of networks containing up-sampling and RNN (Recurrent Neural Network) for feature coding; and inputting three layers of networks including fusion, up-sampling and RNN to perform feature fusion and decoding. And finally, outputting accurate rainfall prediction under the constraint of dynamic weighting loss. According to the method, coding, fusion and decoding of radar and ground station meteorological element data are realized. Under the clamping of a fusion network based on an attention mechanism and dynamic weighting loss, the accuracy of short temporary rainfall prediction is improved, and the performance of heavy rainfall prediction is improved more remarkably.

The invention discloses a measurement error prediction method and system for a current transformer, equipment and a storage medium, and the method comprises the steps: firstly collecting the error data of the current transformer under different test conditions to construct a feature data set, then constructing an error prediction model, training the model through the feature data set, and carrying out the prediction of the error of the current transformer. And finally, based on the current current data, the environment data and the trained model, predicting to obtain a measurement error of the current transformer. The model is trained by taking the environmental data as the features, so that the model can learn the influence of the environmental factors on the measurement error, the model prediction error generated by the influence of the environmental factors is reduced, and the prediction accuracy of the measurement error result is improved. And the hyper-parameters of the model are optimized by adopting a chaos guide adaptive algorithm, so that the optimization speed and the optimization precision of the model parameters are improved, the model network can quickly adapt to the influence of environmental factors on the measurement result of the current transformer, and the prediction accuracy is further improved.

The invention provides a Hammerstein nonlinear dynamic system identification method and system based on an industrial process, and relates to the technical field of system identification, and the method comprises the steps: collecting the historical data of a Hammerstein nonlinear dynamic system as a data set; setting related parameters of the RBF neural network; determining a neuron center point of a hidden layer of the RBF neural network through a K-means algorithm; calculating a standard deviation of a neuron center point through a nearest neighbor Brute method; calculating a hidden layer output matrix of a hidden layer of the RBF neural network; system parameters of the Hammerstein nonlinear dynamic system are determined; an output estimation value of the Hammerstein nonlinear dynamic system is calculated; calculating a mean square error; judging whether the mean square error is smaller than a preset mean square error or whether the number of iterations reaches a preset number of iterations; and if yes, outputting the system parameters as the overall identification result of the Hammerstein nonlinear dynamic system, otherwise, updating the neuron center point, and restarting the calculation of the standard deviation. The method can improve the calculation efficiency and enhance the identification effect and stability of the algorithm.

The invention discloses an energy storage battery health state prediction method, device and equipment and a medium, and the method comprises the steps: collecting operation data of a target battery, carrying out the preprocessing of the operation data, obtaining battery data, and dividing the battery data into a training set and a test set; establishing a Transform model to perform health state prediction on the target battery; a genetic algorithm is adopted, and an optimal hyper-parameter combination of the Transform model is determined; according to the optimal hyper-parameter combination, using the training set to train a Transform model; and inputting the test set into the trained Transform model to carry out health state prediction so as to obtain a health state prediction value of the target battery. According to the method, the prediction precision can be improved, the complexity of model training is reduced, and the accuracy of health state prediction is improved.

The invention discloses a text element recognition method and system based on a multilevel feature fusion adaptive integration technology, which is applied to the field of natural language processing, and comprises the following steps: preprocessing a data text, and training a plurality of text element recognition sub-models based on the preprocessed data text; performing multi-level feature extraction on the preprocessed data text, performing feature fusion, training a weight learning device of a plurality of text element recognition sub-models, and outputting dynamic weights of the plurality of text element recognition sub-models; and based on the output results and the confidence scores of the plurality of text element recognition sub-models, combining with the dynamic weight, and weighting to obtain final text element recognition output. According to the method, the problem of low recognition precision caused by incomplete feature extraction and poor model adaptability in the prior art is effectively solved.

The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a multi-environmental factor and season difference photovoltaic ultra-short-term power prediction and correction method, which comprises the following steps of: 1, performing correlation analysis, and screening out main environmental factors influencing photovoltaic power; 2, performing signal feature extraction on the photovoltaic time sequence data by utilizing VMD to obtain components which are high in stability and contain different frequencies, and performing primary prediction on each component by adopting a CNN-BiGRU-Attention model; and step 3, carrying out secondary prediction on the error sequence by using VMD-SSA-LSSVM, and correcting the result of the primary prediction, the design is reasonable, a prediction model optimization strategy considering seasonal differences is provided, photovoltaic output has obvious periodicity, a photovoltaic output probability prediction model is established on the premise of considering seasons, model parameters are optimized, and the prediction efficiency is improved. Therefore, the prediction precision of the prediction model in each season is optimal.

The invention discloses a fuzzy evolutionary algorithm-based distributed photovoltaic and electrical load decoupling bus load prediction method and system for a power grid planning theme dynamic iteration method, and relates to the technical field of power dispatching. Comprising the steps of obtaining historical operation data of a to-be-predicted bus region; establishing a photovoltaic power generation and electrical load decoupling model of the access interval; dividing the bus load into time intervals according to the installed capacity proportion; constructing an electrical load prediction reference model of the non-accessed section; performing bus load prediction based on the decoupling model; and performing accuracy evaluation and iterative optimization on the prediction model. According to the method, the reliability and accuracy of prediction are ensured by acquiring the historical operation data and establishing the reference model, the data utilization efficiency is improved, the time intervals are divided according to the installed capacity proportion, the decoupling model of the distributed photovoltaic and electrical load is established, corresponding prediction strategies are adopted in different time intervals, and the adaptability of prediction is improved.