365 results about "Interval prediction" patented technology

The invention discloses a new energy power interval prediction method for mining correlation between seasonal change characteristics and meteorological characteristics. The method comprises the following steps: 1) obtaining original photovoltaic power data; 2) decomposing the original photovoltaic power data by using a moving average algorithm to obtain a trend component and a seasonal component; 3) acquiring meteorological data, and constructing a trend component prediction model and a seasonal component prediction model; 4) inputting the meteorological data and the seasonal component into the seasonal component prediction model to obtain a seasonal prediction result; 5) inputting the trend component into the trend component prediction model to obtain a trend prediction result; and 6) calculating the sum of the seasonal prediction result and the trend prediction result to obtain a final photovoltaic prediction result. According to the invention, weather information is allowed to influence seasonal components of photovoltaic power generation data without influencing trend components. The invention further provides a seasonal component prediction unit with a double-layer hierarchical attention mechanism, and attention to the relation among meteorological features, key time nodes and seasonal components is enhanced.

The invention discloses a photovoltaic output hybrid probability interval prediction method and system based on a parallel deep learning architecture. The method comprises the following steps: constructing a photovoltaic output multi-source driving factor set; constructing an original feature matrix based on the photovoltaic output multi-source driving factor set; performing spatial-temporal feature parallel decoupling on the original feature matrix, and inputting the decoupled time features and spatial features into a spatial-temporal feature complementary enhancement module for fusion; inputting the photovoltaic output spatial-temporal feature matrix after feature enhancement into a photovoltaic output reference type prediction model to obtain a photovoltaic output reference type prediction result and a corresponding error; inputting the photovoltaic output reference type prediction result errors into the risk type prediction model, calculating prediction error risk interval boundary values, and superposing the prediction error risk interval boundary values to the reference type prediction result to obtain respective photovoltaic output risk type prediction results; and constructing a photovoltaic output hybrid risk type prediction framework, inputting two risk type prediction model results for coupling and optimization, and obtaining a photovoltaic output hybrid risk type prediction result.

The invention discloses a GA-LSTM-GPR-based photovoltaic inverter fault diagnosis method, and the method comprises the steps: collecting the fault data of a photovoltaic inverter, and carrying out the preprocessing of the fault data through the filling of missing values, normalization and standardization; a Pearson correlation coefficient and a Spearman correlation coefficient are adopted to analyze correlation between features in the fault data, and a recursive feature elimination (RFE) method is established based on a random forest RF to extract important features; the traditional LSTM network is improved by adding an attention layer, and the attention weight is optimized by adopting a genetic algorithm GA, so that the overall performance and prediction accuracy of the model are improved; and on the basis of the first prediction result of the GA-LSTM model, a fault diagnosis model of the photovoltaic inverter is constructed by combining Gaussian process regression GPR. According to the photovoltaic inverter fault diagnosis method provided by the invention, not only can the complex nonlinear dynamic relationship among the feature data be captured, but also the distribution condition of prediction results can be known through comprehensive point prediction, interval prediction, probability prediction and quantification uncertainty, so that more comprehensive and reliable fault diagnosis information can be provided.

The invention provides an intraoperative hypotension early warning method and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: processing a preset hypotension early warning model based on a training sample set with identification information to generate a target hypotension early warning model; processing the operation information of the target user in a preset time period, and generating an interval prediction sub-task and an intra-operation prediction sub-task; processing the interval prediction subtask and the intra-operative prediction subtask to generate a preset prediction feature vector; processing the physiological motion parameter information of the target user to generate a blood pressure limiting factor of the target user; processing the physiological state information of the target user to generate an ambulatory blood pressure influence factor of the target user; and processing the preset prediction feature vector, the blood pressure limiting factor of the target user and the ambulatory blood pressure influence factor of the target user based on the target hypotension early warning model to generate hypotension early warning information of the target user.

The invention provides a consumption reduction method and device for a wind and light energy storage complementary thermal power plant system, and the method comprises the steps: obtaining the wind speed, illumination intensity, energy storage charge state, auxiliary power load curve, power grid dynamic electricity price signal and thermal power generating unit auxiliary machine operation parameters of the thermal power plant system, and taking the parameters as a data set; based on a weather prediction model, a load prediction model and an electricity price fluctuation model, generating a wind power generation output prediction value, a photovoltaic power generation output prediction value, a load demand prediction value and an electricity price interval prediction value in a future preset time period according to the data set, and taking the prediction values as prediction results; by taking minimization of plant power cost and maximization of renewable energy consumption as targets, generating an energy storage charging and discharging instruction, a wind power generation and photovoltaic power generation grid-connected priority sequence and a thermal power auxiliary engine regulation and control strategy according to the data set and the prediction result to serve as control instructions; control instructions are executed through control equipment in the wind-solar energy storage complementary thermal power plant system, and energy conservation and consumption reduction are achieved by integrating wind energy, solar energy and an energy storage system.

The invention relates to the technical field of medical data processing, in particular to a postmenopausal osteoporosis patient medical data analysis method and system, and the method comprises the following steps: extracting a bone mineral density measurement value and a bone metabolism index concentration based on patient bone mineral density data, bone metabolism index data and fracture event records; according to the method, through combination of covariance matrix calculation, feature value and feature vector extraction and revealing of deep relevance between data, fine description of bone mineral density and metabolism index changes is achieved, state probability values, fracture records and drug use data are subjected to normalization weighting processing, and an optimized posterior state distribution matrix is generated; the accuracy and comprehensiveness of data fusion are improved, a key dynamic feature set is extracted through screening and accumulation operation, the pertinence and efficiency of analysis and normalization and time interval prediction of a trend curve are optimized, a quantitative result of the bone mineral density change trend and the fracture risk is formed, and high-precision support is provided for illness state assessment and personalized analysis.

The invention discloses a long-tail SKU sales prediction method and system, and belongs to the technical field of sales prediction, and the method comprises the following steps: obtaining SKU sales feature data, and carrying out the preprocessing of the sales feature data; obtaining a multi-dimensional feature table based on the standardized training data set; a rule engine and a lightweight classification model are used for joint judgment to classify the multi-dimensional feature table, and four SKU category labels and probability vectors thereof are output; differentiated training models of the four SKU category labels are established, different types of SKUs are routed to the corresponding training models, and SKU sales volume point predicted values are output; probability calibration is carried out, and a plurality of quantiles are calculated to form interval prediction; establishing an SKU multi-hierarchy relationship, and performing hierarchy consistency solution on prediction results among multiple hierarchies; in the online operation process of the prediction model, input data distribution drift is monitored, and model updating is triggered when the input data distribution drift exceeds a threshold value. According to the method, the prediction precision and stability of the long-tail SKU are improved, and rapid adaptive prediction of sudden and off-peak demands is realized.

The invention discloses a photovoltaic power interval prediction method based on a GRU-LSTM combined neural network, and belongs to the technical field of photovoltaic power interval prediction. The method comprises the following steps: S1, taking historical power generation data as original wind-solar power generation power prediction data, processing the data, and screening related meteorological characteristics by adopting a Pearson correlation coefficient; s2, a GRU-LSTM combination model is constructed, and related hyper-parameters are set; s3, taking the screened related meteorological features as input for training, calculating a photovoltaic point prediction result according to a weight coefficient, and performing related error evaluation; and S4, based on the photovoltaic power point prediction result, calculating a photovoltaic power interval prediction result by using a quantile regression technology, and detecting performance evaluation through a test set. According to the method, the minimum prediction error correlation index is taken as the target, the influence of different weathers on photovoltaic power processing is considered, the Pearson's correlation coefficient analysis is utilized to select more representative meteorological characteristics, and the combined model and the quantile regression technology are utilized to finally obtain the photovoltaic power interval prediction result.

The invention relates to a wind power interval prediction method based on deep learning and Markov chain Monte Carlo. The method comprises the steps of carrying out feature extraction on historical wind power generation data by utilizing LSTM, constructing a Gaussian likelihood function based on a mean value and a variance according to hidden layer information representation, carrying out modeling processing on the Gaussian likelihood function by utilizing negative binomial distribution, optimizing hidden layer parameters of the LSTM by utilizing a loss function of a probability density function obtained through design, and obtaining hidden layer parameters of the LSTM. Obtaining a mean value and a variance of the optimized Gaussian likelihood function; a Markov chain is constructed according to the distribution characteristics of data characteristics represented by the mean value and variance of the optimized Gaussian likelihood function by adopting a Markov chain Monte Carlo algorithm to randomly generate a large number of samples, so that the stable distribution of the samples is the same as the known distribution; and taking a prediction interval with a predetermined size as a final prediction result for the distribution of the samples which most accord with the historical data characteristics. By adopting the method, the problem that a large amount of historical data cannot be effectively utilized by probability prediction can be solved.

The invention discloses a source-load interval prediction method based on the combination of SWLSTM and integrated learning, and the method comprises the following steps: S1, obtaining an original data set, carrying out the dimension reduction of the original data set based on local linear embedding, and carrying out the clustering of the data set after the dimension reduction based on a k-means algorithm, and obtaining a feature day data set; s2, training each base learner based on the feature day data set, respectively calculating Q statistical values among the results of each base learner, and screening out a base learner pair with the minimum Q statistical value as a base learner model; s3, taking the base learner model as a first-layer prediction model of an integrated learning framework, taking the SWLSTM neural network as a second-layer prediction model, and introducing a confidence interval to obtain prediction uncertainty information; and S4, based on the point prediction result and the uncertainty prediction result obtained by prediction in the step S3, evaluating the prediction result by adopting a point prediction evaluation index and an interval prediction index respectively.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, and the method comprises the steps: wind power data collection and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, construction of a short-term power prediction model, and short-term power prediction. According to the method, the historical window length and the decomposition scale can be autonomously adjusted according to the inherent fluctuation characteristics of the data, and multi-scale accurate characterization of the non-stationary power sequence is realized; according to the method, a dynamic space-time diagram fusing geographic distance and instantaneous power correlation is constructed, and a diagram attention network combined with trend similarity gating is designed, so that dynamic refined modeling of a space incidence relation is realized; according to fluctuation intensity self-adaptive loss function dynamic balance point prediction precision and interval prediction reliability, synchronously outputting deterministic and probabilistic prediction results; the rated power limit and the ramp rate constraint are embedded into the model in a soft mode, and it is ensured that the prediction result conforms to the actual operation rule of the wind turbine generator.

The invention provides a virtual marshalling train interval prediction method and system based on a high-order extended Kalman smoother, and belongs to the technical field of train operation management, real-time state data of a train in marshalling and historical state data of a leading train are obtained, and the state data comprise speed data, position data and acceleration data; on the basis of the obtained real-time state data of the vehicle and the historical state data of the leading vehicle, a high-order extended Kalman filter is adopted to predict the dynamic state of the vehicle; and based on the state after smooth optimization, dynamically calculating the train spacing based on the difference value of the position states of the leading train and the following train. According to the method, the real-time dynamic tracking of the train state and the high-precision estimation of the distance between the train and the head train are realized, and through the definition of the high-order hidden variables and the pseudo-linearization representation of the high-order hidden variables, the description capability of the nonlinear characteristic of the train system is improved, the calculation complexity of high-order expansion is reduced, and the robustness and the stability of the virtual coupling train system are ensured.

The invention discloses an optical power prediction method based on multi-site spatio-temporal characteristics and dynamic optimization. The method comprises the following steps: collecting data of a plurality of monitoring sites and carrying out characteristic selection by using a selection-deletion characteristic selection method; carrying out weighted fusion by adopting a multi-site data weighted fusion strategy; constructing an interval prediction model, inputting data subjected to feature selection and weighted fusion into an STG-Mamb-neural network model, and then performing LUBE interval prediction on output to obtain upper and lower boundaries of optical power; introducing an improved weighted average optimization algorithm, and optimizing the interval prediction model in combination with random disturbance and a boundary constraint strategy; and evaluating the optimized interval prediction model by adopting a cross validation method, and predicting the optical power of the multiple sites after the optimal interval prediction model is obtained. According to the invention, stable prediction performance can be maintained in a changeable environment, so that the reliability and stability of optical power prediction are improved.

The invention relates to a photovoltaic power interval prediction method based on time sequence decomposition and conformal quantile regression, and the method comprises the steps: collecting historical power data, solar irradiance data, temperature data, relative humidity data and wind speed data of a photovoltaic power station, supplementing missing values in the photovoltaic power data through employing a linear interpolation method, and carrying out the prediction of the photovoltaic power interval. Secondly, normalizing all data by using a Z-Score standard method, dividing the processed data into a training set, a calibration set and a test set, constructing a time sequence decomposition model based on a NeuralProphet framework, obtaining a deterministic prediction model, training two conditional quantile models respectively by using a quantile regression method, and obtaining a time sequence decomposition model based on the NeuralProphet framework; the method comprises the following steps: determining a power interval upper limit and lower limit prediction model according with a preset confidence level, deploying the model in a calibration set, calculating to obtain an empirical quantile, inputting test data into the constructed model, determining a final prediction interval, and realizing interval prediction of ultra-short-term photovoltaic power.

The invention relates to the technical field of power battery life prediction, and discloses a power battery residual life prediction method based on sensor fusion. The method comprises the steps of collecting and processing multi-dimensional electrical sensor signals of historical operation of a battery, and generating a voltage platform change characteristic spectrum, a current stress characteristic spectrum and an internal resistance component evolution spectrum. And inputting the maps into a feature space alignment network for space-time dimension registration and feature cross validation to generate a unified electrochemical state panoramic feature map. Based on the atlas, multi-stage battery life state backtracking and deduction are realized by constructing a dynamic attenuation trajectory tree, and a battery aging state vector set is output. After working condition constraint correction, the set drives a residual life interval prediction module, boundary estimation and probability density propagation calculation are fused, and finally a confidence interval of the residual cycle life and probability density distribution of the confidence interval are generated. According to the method, the precision of multi-source information fusion and the reliability of life prediction are improved.

The invention discloses a reservoir landslide displacement interval prediction method fusing BiLSTM and Transformer, and the method comprises the following steps: obtaining landslide multi-source monitoring data, and reconstructing displacement time series data into trend term displacement and periodic term displacement through employing empirical mode decomposition; performing interval prediction on trend term displacement by adopting a Holt two-parameter exponential smoothing method and a kernel density estimation method; after a trend term displacement point prediction result is obtained, interval prediction is carried out by adopting a kernel density estimation method, and a trend term displacement interval prediction result is obtained; a BiLSTM-Transformer model and a kernel density estimation method are adopted to carry out interval prediction on the displacement of the periodic term; and finally, superposing the trend term displacement interval prediction result and the periodic term displacement interval prediction result to obtain an accumulated displacement interval prediction result. According to the method, the accuracy and the efficiency of slope displacement prediction are improved by capturing the change before and after the time sequence data through the BiLSTM, a Transform multi-head attention mechanism and parallel calculation.

The invention relates to the technical field of photovoltaic power prediction, and provides a distributed photovoltaic power prediction method and system based on machine learning. The method comprises the following steps: interacting grid-connected position nodes and installation parameters of photovoltaic base stations in a photovoltaic jurisdiction area, and determining a distributed topology; mining photovoltaic power elements, taking modal decomposition-reconstruction-prediction as underlying logic, developing a power prediction module, and carrying out convergence learning and embedded connection; accessing a front-end acquisition card, and constraining and acquiring a sampling matrix of photovoltaic power elements through a timestamp; and importing the sampling matrix into a power prediction module, performing interval prediction decision through dynamic and static combination, and determining a power triple. According to the method, the technical problem of insufficient prediction precision caused by complex topological structure and data acquisition asynchronism of a distributed photovoltaic system is solved, and the technical effect of high-precision and robust power prediction is achieved through distributed topology construction, a modal decomposition-reconstruction prediction mechanism and dynamic and static combined interval prediction decision.

The invention discloses a short-term photovoltaic power deep learning prediction method based on decomposition sequence complexity evaluation and clustering reconstruction. The short-term photovoltaic power deep learning prediction method mainly comprises the following steps: step 1, decomposing a photovoltaic power time sequence by using successive variational mode decomposition; 2, calculating the complexity of each decomposition component by using a fuzzy entropy algorithm; 3, dividing the components with different complexities by using Gaussian hybrid clustering, and reconstructing the components into high-frequency, intermediate-frequency and low-frequency components; step 4, using a chaos evolution optimization algorithm to optimize hyper-parameters of the bidirectional gating cycle unit (BIGRU) prediction model of each component; 5, performing short-term photovoltaic power prediction on the reconstructed high-frequency component, the reconstructed intermediate-frequency component and the reconstructed low-frequency component by adopting a BIGRU model; and step 6, superposing the predicted values of the components to obtain a photovoltaic power point prediction result. And step 7, using kernel density estimation to form a confidence interval based on a point prediction result, and finally obtaining a photovoltaic power probability interval prediction result. The method has the beneficial effects that the complexity of each decomposition component is evaluated and reconstructed by utilizing the fuzzy entropy algorithm, and comprehensive optimization of each link including model training is realized on the basis of powerful capabilities of capturing context information and processing long sequence data based on the BIGRU.

The invention provides a photovoltaic output interval prediction method based on CEEMDAN-DBO-BiLSTM, and the method comprises the following steps: S1, decomposing a photovoltaic power sequence into a plurality of mode components through an adaptive noise complete set empirical mode decomposition method according to the nonlinear and non-stationary characteristics of distributed photovoltaic historical data; s2, carrying out secondary decomposition on the high-frequency non-stationary component obtained by primary decomposition, and reconstructing all components into a trend component and an oscillation component by adopting a sample entropy method; s3, optimizing parameters of the bidirectional long-short-term memory network based on a dung beetle optimization algorithm, and finally obtaining distributed photovoltaic point predicted values of the two components; and S4, probability density estimation is performed on the point prediction error of the oscillation component based on a kernel density estimation method, and the point prediction values are superposed to obtain an overall prediction interval result, so that the complexity of the original prediction component is significantly reduced, and the prediction precision of the model is further improved.

The invention belongs to the technical field of electric power transaction, and particularly relates to an electric power spot transaction clearing and bidding optimization method based on computing power prediction, which comprises the following steps: collecting and preprocessing multi-source data, and screening a core feature set through mutual information entropy, Pearson's correlation coefficients and variance expansion factors; a CNN-LSTM attention model is built, and output load, power output and electricity price fluctuation interval prediction results are calibrated through error feedback; a dynamic bidding decision mathematical model is constructed, and elastic constraints are set in combination with prediction parameters; solving by adopting an improved particle swarm algorithm, and outputting an optimal bidding strategy; a node marginal electricity price mechanism is fused for accurate clearing; and verifying a clearing result in a layered manner, and performing deviation feedback iterative optimization. According to the method, a whole-process closed-loop mechanism is constructed, the prediction precision and the bidding and clearing collaboration are improved, the system operation cost is reduced, the transaction real-time requirement is met, the power grid safety and the market subject income are guaranteed, and the method has important practical value.

The invention discloses a monthly runoff sequence interval prediction method which comprises the following steps: S1, monthly runoff data preparation: collecting monthly runoff data, preprocessing the data, and dividing the data into training data and test data; s2, establishing a CNN-BiLSTM point prediction model which comprises an input layer, a CNN layer, a BiLSTM layer and an output layer, and inputting the training data into the CNN-BiLSTM point prediction model to obtain a monthly runoff point prediction result; s3, a CPO-CNN-BiLSTM point prediction model is established; s4, a CPO-CNN-BiLSTM-NKDE interval prediction model is established, and the CPO-CNN-BiLSTM Aiming at the problem that hyper-parameters are difficult to select in the CNN-BiLSTM point prediction model, the CPO is used for optimizing the hidden layer node number, the initial learning rate and the regularization coefficient in the CNN-BiLSTM model, so that the optimal CPO-CNN-BiLSTM point prediction model is established, and the convergence rate and the accuracy of monthly runoff prediction are greatly improved.

The invention is suitable for the technical field of power system scheduling, and provides a new energy access provincial power grid multi-time scale intelligent scheduling method, which comprises the following steps of: obtaining related data of a provincial power grid, generating a new energy power point prediction curve of a day-ahead time scale, a new energy power fluctuation interval prediction result of an intra-day time scale, a new energy power point prediction curve of a real-time time scale and a new energy power fluctuation interval prediction result of the current moment by using the trained multi-time scale prediction model; processing by the day-ahead deep reinforcement learning agent, and outputting a day-ahead scheduling instruction; processing by the intraday deep reinforcement learning agent, and outputting an intraday adjustment instruction; and a real-time control instruction is output after the real-time deep reinforcement learning intelligent agent processes the real-time control instruction. According to the method, global optimization from day-ahead robust planning and intra-day active prevention to real-time multi-target cooperation is realized, and the cooperative scheduling control level of scheduling decision is improved.

The invention discloses an electric power spot day-ahead electricity price prediction method based on bidding space analysis, and the method comprises the steps: constructing input features, obtaining electric power market data, meteorological data and date data, and constructing a multi-dimensional input feature system; data preprocessing: carrying out missing value processing, abnormal value elimination, feature normalization and category feature coding on the data in the multi-dimensional input feature system to form a standardized feature matrix; model training: carrying out training by adopting a deep learning model, extracting a multi-scale nonlinear relationship between the bidding space and the electricity price, and fusing time sequence dependence and global feature interaction; and electricity price prediction: outputting a point prediction result and an interval prediction result of the electricity price by using the trained model. According to the method, a multi-dimensional feature system is constructed, and a model is used for training after preprocessing so as to synchronously output day-ahead electricity price point prediction and interval prediction. And high-precision and self-adaptive high-value electricity price prediction with risk assessment capability is realized.

The invention relates to the technical field of electricity price interval prediction, in particular to a long-short-term memory neural network electricity price interval prediction method considering multi-source data feature selection and quantile, comprising the following steps: S1, feature selection of multi-source data of electricity price: constructing a multi-source feature factor set; s2, data normalization: performing maximum and minimum normalization processing on the selected characteristic factors and the electricity price data; s3, constructing a quantile-based long and short-term memory neural network prediction model: constructing a quantile regression model based on a long and short-term memory network, and capturing change characteristics of the electricity price; s4, interval prediction based on kernel density estimation: generating a probability density function of the electricity price by adopting a Gaussian kernel function to realize interval prediction of the electricity price; according to the invention, resource configuration is optimized, operation efficiency is improved, stability of power supply is ensured, and stability and fairness of the energy market are promoted.

The invention relates to the technical field of power system optimization scheduling, and discloses a source network load storage optimization scheduling method suitable for flexible supply and demand balance in extreme weather. The method comprises the following steps: firstly, acquiring new energy power interval prediction data which aims at typical extreme weather scenes and is subjected to error correction; on the basis, the flexibility requirement of the system and the supply capability of multi-type resources are quantified; and furthermore, with maximization of the comprehensive benefit of the system as a target, a flexible vacancy virtual penalty term for stabilizing fluctuation risks caused by extreme weather is particularly introduced, an optimal scheduling model is constructed and solved, and a scheduling plan is generated. According to the method, by fusing extreme weather exclusive prediction information and an operation risk hedging mechanism, efficient coordination of source network load storage resources in extreme weather is realized, the new energy power abandoning rate is effectively reduced, the dependence on traditional thermal power is reduced, and the comprehensive operation benefit and safety margin of the system are improved.

The invention belongs to the related technical field of power system calculation, and discloses a day-ahead market clearing price interval prediction method considering source load uncertainty, which comprises the following steps of: taking the source load uncertainty as a main variable; multi-dimensional parameter random distribution characteristics of power generation end unit output prediction and uncertainty of load end demand response effect are quantified, a source load output uncertainty interval is discretized, a discrete point is used as a constraint variable, and the minimum system power generation total cost is used as a target. Establishing a dynamic influence change model considering source load uncertainty and a day-ahead market clearing price interval, traversing each source load discrete output scene and substituting into the model, cyclically calculating node marginal electricity prices under different scenes, and counting and predicting a clearing electricity price interval as a prediction result. According to the method, the clearing price range under the influence of the source load uncertainty can be accurately calculated, the unit quotation income and the power system safety are improved, the quotation risk is reduced, and a reference is provided for a bidding strategy of a power generation manufacturer.

The invention discloses a regional wind-solar combined power interval prediction method and system based on multi-task learning and a hybrid neural network, and relates to the technical field of combined prediction. Meteorological data and wind-solar historical power data of a to-be-researched region are collected, and a historical data set is constructed; preprocessing the historical data set, and performing feature selection by adopting a predictive ability scoring algorithm to obtain screened meteorological data and historical power data; constructing a joint power interval prediction model; and performing combined power generation prediction based on the trained combined power interval prediction model. According to the method, the space-time correlation and the complementary relation of wind energy and photovoltaic energy are excavated, the joint power interval prediction model is constructed, and the power prediction precision is improved.

The invention discloses an active regulation and control method, device and equipment for source load matching of an integrated energy system and a storage medium, and relates to the technical field of operation regulation and control of the integrated energy system. The method comprises the following steps: predicting upper and lower boundary quantile change intervals of a source load of the integrated energy system in a preset time period through a source load interval prediction model to obtain predicted fluctuation boundary information; carrying out capacity configuration optimization on energy equipment in the integrated energy system based on a passive load following strategy to obtain optimized capacity parameters of the energy equipment; and combining the predicted fluctuation boundary information and the optimized capacity parameters to formulate an operation regulation and control strategy of the energy equipment, and performing automatic matching on the source load of the integrated energy system according to the operation regulation and control strategy. According to the invention, the impact of the uncertainty of the source load on the system is reduced through the quantile fluctuation boundary interval, and then the active matching of the source load is realized based on the high-precision predicted fluctuation boundary information and the optimized capacity parameters, thereby effectively coping with the load fluctuation, and improving the reliability and benefit of the operation of the system.

The invention discloses a short-term power load interval prediction method considering interval construction. A power load prediction TCN-EAD network model is constructed and trained, the trained TCN-EAD network model is utilized to obtain power load point prediction, a proportionality coefficient method is adopted to construct a prediction interval, and a prediction interval satisfaction index PISI is constructed; obtaining optimal hyper-parameters of the TCN-EAD network model by adopting a WOA optimization algorithm, and calculating an optimal proportionality coefficient of a prediction interval at the same time; and obtaining point prediction by using the optimal hyper-parameter TCN-EAD network model, and constructing an optimal prediction interval of the power load according to the optimal proportionality coefficient. The method has good adaptivity and accuracy, provides reliable load change information for power grid operation scheduling, is suitable for complex and changeable user power consumption scenes, quantifies the uncertainty in power load prediction on the basis of a short-term power load point prediction method, and improves the prediction accuracy. The purposes of controlling the prediction interval width, simplifying the principle, reducing the calculation cost and the like are achieved.