197 results about "Price prediction" patented technology

The invention belongs to the technical field of intelligent heat supply, and provides a green power consumption heat storage and release matching control method and system based on a water heat storage boiler, and the method comprises the steps: firstly obtaining a heat load prediction sequence and an electric power spot market price prediction sequence of a heat supply system; secondly, regarding the heat supply network as a graph structure composed of nodes and pipelines, and establishing a pipe network dynamic model for describing the hydraulic and thermal characteristics of the heat supply pipe network; and finally, with the purpose of minimizing the comprehensive operation cost of the heat supply system in the whole scheduling period, and with the whole network heat balance, heat source operation, centralized heat storage device operation, heat supply network safety and user comfort as constraint conditions, solving to obtain a source network load storage cooperative control strategy. The purpose of integrated collaborative optimization of the heat source, the energy storage, the pipe network and the load is achieved, a scheduling strategy with better economical efficiency can be generated, and the problems that the absorption capacity is insufficient, and the overall operation economical efficiency of the system is not high are solved.

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 discloses an intelligent energy-saving scheduling system and method based on an industrial computing power data center, and the method comprises the steps: collecting all kinds of operation data in the data center and external environment data in real time, and carrying out the preprocessing and cleaning of the data; using a machine learning algorithm to predict future load demand and energy price fluctuation; in combination with load prediction and energy price prediction, a multi-objective optimization algorithm is adopted to generate a resource scheduling plan; dynamically adjusting the working state of the cooling system according to the load change and the environment temperature and humidity data; executing a task according to the scheduling plan, feeding back according to the real-time monitoring data and an execution result, and adjusting a scheduling strategy; according to the intelligent energy-saving scheduling system and method based on the industrial computing power data center, by introducing an intelligent algorithm and multi-source data analysis, the energy utilization efficiency of the data center is improved, the operation cost is reduced, and optimal scheduling is achieved under the condition of dynamic load and energy price fluctuation.

The invention relates to the technical field of real estate, and discloses an ant colony algorithm and neural network combined house price data prediction method, which comprises the following steps: S1, multi-source data acquisition, S2, data preprocessing, S3, ant colony algorithm parameter optimization, S4, neural network dual-stage training and S5, adaptive optimization feedback. According to the house price data prediction technology combining the ant colony algorithm and the neural network, neural network model parameters are optimized by adopting the ant colony algorithm; in the house price prediction model, the parameters are combined to form a path, and ants traverse different parameter combination paths; along with iteration, pheromones on a better parameter combination path are continuously accumulated and enhanced, and ants are guided to explore more; in this way, the parameter space can be fully traversed, local optimum caused by parameter random initialization of a traditional neural network is avoided, and therefore the model can obtain a better parameter combination, and the prediction precision is remarkably improved.

The invention discloses an electric power price prediction method and device based on time dimension, equipment and a storage medium, and relates to the technical field of electric power systems, and the method comprises the steps: carrying out the preprocessing of a multi-source data set comprising historical electric power transaction data, a load demand curve, energy production data and fuel cost, performing feature extraction on the obtained processed data set to obtain each data feature, and determining a training set and a verification set based on the data features by using Bernoulli distribution; constructing an initial power price prediction model based on a multi-head attention mechanism, a convolutional layer, a preset activation function, a bidirectional long-short-term memory network and integration of a low-rank adaptation technology, and training and verifying the initial power price prediction model by using the training set and the verification set respectively; and performing multi-scale prediction by using the obtained target power price prediction model to obtain prediction results of the power price in different time dimensions. And the real-time, day-ahead and weekly prediction accuracy of the electric power price is improved.

The invention discloses an electricity price prediction method and system based on fluctuation characteristic decoupling, and the method comprises the steps: obtaining electricity market data including historical electricity prices and exogenous variable characteristics, and carrying out the data preprocessing; decoupling the preprocessed historical electricity price data into an input sequence for regression prediction and a label sequence for classification prediction; predicting a regression electricity price in a future time period and predicting a time period when a low electricity price event occurs; and integrating the regression electricity price of the future time period and the time period in which the low electricity price event occurs, carrying out cascade connection to obtain a final future electricity price prediction sequence, and outputting the final future electricity price prediction sequence. According to the scheme of the invention, a conventional single and difficult electricity price prediction problem is decomposed into a conventional regression prediction task and a special low-electricity-price event classification task, so that the prediction accuracy of extremely low-price events such as zero electricity price and negative electricity price can be remarkably improved.

The invention discloses an aviation equipment intelligent pricing method and system and a medium, and the method comprises the steps: carrying out the fusion processing and feature extraction of multi-source heterogeneous data, such as aviation material internal transaction data, external market information data, supply chain state data, and regulation event text data, and generating a standardized feature vector; utilizing a natural language model to extract structured event features from the law and regulation event text data; inputting the standardized feature vector into a pre-training price prediction model adopting a mixed architecture of an integrated learning algorithm and a time sequence deep learning algorithm, and outputting a reference price prediction value and a price prediction interval; according to a pricing strategy instruction selected by a user, a final pricing result is generated through a strategy mapping module, and an interpretable price cause analysis report is output, so that the accuracy and adaptability of aerial material pricing are effectively improved, the dynamic response of various pricing strategies is supported, the transparency and interpretability of a decision-making process are enhanced, and the user experience is improved. And scientific and reliable intelligent decision support is provided for aviation equipment pricing.

The invention discloses an electric power intra-day price prediction method based on dynamic holiday weight and multi-source fusion, and the method comprises the steps: obtaining multi-source historical data, and carrying out the time synchronization processing; holiday and festival time information is acquired, and a dynamic weight is generated based on the influence of holidays and festivals and upstream and downstream dates on the power load and the electricity price; constructing a multi-dimensional predictive factor matrix based on the multi-source historical data after time synchronization processing; and according to the multi-dimensional predictive factor matrix, on the basis of a collaborative optimization multi-model combination comprising a feedforward neural network model and a bagged regression tree model, intra-day joint prediction is executed, the intra-day joint prediction refers to a process of predicting the power load and the electricity price hourly, and an hourly prediction result of the power load and the electricity price is output. A dynamic holiday weight mechanism is introduced, a multi-source fused high-dimensional predictive factor matrix is constructed, and a multi-model combined predictive strategy of collaborative optimization of a feedforward neural network and a bagged regression tree is adopted, so that the precision and stability of intra-day electricity price and load prediction of the electricity market are improved.

The invention discloses an apple industry chain multi-source heterogeneous data coupling prediction and early warning method and system, and belongs to the field of agricultural information technology and intelligent prediction.The method comprises the steps that price data, weather data, Internet of Things sensor data and inventory data of an apple industry chain are collected, and vectorization is conducted after cleaning and dynamic time warping alignment; calculating a disaster influence factor alpha and a price conduction time lag tau based on the disaster event; inputting the multi-source data vector into a prediction model fused with bidirectional LSTM, multi-head cross attention and disaster sensing gating modules, and outputting a price prediction sequence; the invention aims to solve the problems of single data, lagging disaster response and the like in the prior art.

The invention relates to the technical field of electricity price prediction, in particular to an electricity market long-period simulation-based medium and long-term price prediction method, which comprises the following steps of S1, market basic data acquisition: acquiring whole market basic data information; s2, unit commitment model construction and day-ahead simulation: constructing a unit commitment model considering power grid security constraints; s3, economic dispatching model construction: constructing an economic dispatching model considering power grid security constraints; s4, node marginal electricity price and load flow calculation: node marginal electricity price calculation and load flow calculation are carried out; s5, uncertainty factor evaluation: the uncertainty factors existing in the electricity market are evaluated; s6, analyzing technical performance and economic indexes: analyzing statistics of various technical performance and economic indexes of the system; according to the method, the stability and the reliability of an enterprise under an emergency situation are enhanced, so that the enterprise can better adapt to market changes, and the scientificity and the accuracy of decision making are improved.

The invention discloses an asset price prediction method and device and electronic equipment, and the method comprises the steps: obtaining the feature information of a target asset in a first preset time range, and enabling the first preset time range to comprise a plurality of continuous preset time periods, the plurality of continuous preset time periods comprise a first number of historical time periods and a second number of prediction time periods of the to-be-predicted asset price growth rate, the feature information corresponding to each historical time period comprises price information and time information, and the feature information corresponding to each prediction time period comprises time information; determining a first input sequence and a second input sequence based on the feature information of the target asset in the first preset time range; and inputting the first input sequence and the second input sequence into an asset price prediction model to obtain an asset price growth rate, corresponding to the second number of prediction time periods, of the target asset output by the asset price prediction model.

The invention provides a clearing price interval prediction method based on an LSTM network and a similar point matching algorithm, and the method comprises the following steps: carrying out the optimization of an independent variable feature set, taking a real-time clearing price as a target variable based on the historical data of an electricity market at an interval of 15 minutes, constructing a point prediction model based on the LSTM network, and carrying out the calculation of the point prediction model. On the basis of optimization of an independent variable feature set, a long-short-term memory neural network is adopted to construct a real-time clearing price point prediction model, high-precision point prediction, similar point matching and statistical feature extraction are realized, feature vectors of to-be-predicted time points are constructed to be used for matching similar time points in a historical data set, and a real-time clearing price point prediction model is obtained. And constructing upper and lower limits of the prediction interval. A real-time clearing price point prediction model is constructed based on an LSTM network, and an upper limit and a lower limit of a real-time clearing price prediction interval are formed in combination with a similar point matching algorithm and an interval feature statistical analysis method, so that uncertainty in an electricity market is quantified better, and more decision bases are provided for market participants.

The invention relates to an energy storage system capacity configuration method based on dual-mode prediction, and solves the problem of wind curtailment and electricity curtailment caused by insufficient energy storage residual capacity due to easy zeroing of the noon clearing electricity price compared with the prior art. The method comprises the following steps: obtaining historical transaction data and historical climate data of a regional electric power spot market; constructing an electricity market transaction distribution time period prediction model and training electricity price return-to-zero time period prediction; the electricity market transaction distribution time period prediction model trains the data prediction electricity price based on the predicted time period; acquiring real-time data and outputting a power market transaction distribution period prediction model result; and dynamically configuring the capacity of the energy storage system. The electricity price fluctuation condition under the current marketization transaction is considered, the BP neural network algorithm is combined to predict the electricity price return-to-zero interval in the noon power generation peak period, the all-day electricity price is predicted according to the result, the energy storage capacity is adjusted accordingly, and the method has the technical advantages of being scientific and reasonable, high in applicability, high in adjusting speed and the like.

The invention belongs to the technical field of big data, and particularly relates to a poultry egg price prediction method and system, and the method comprises the steps: firstly collecting full-dimensional data based on multiple channels, including a full-cycle industrial chain, a priority sample library, a regional consumption behavior, a store geographic attribute, a market, sales data and a real-time data stream; preprocessing the collected data to generate preprocessed full-dimensional data; a Transform-based poultry egg price prediction model is constructed, wherein the model comprises an encoder, a decoder and eight special attention heads for price short-term fluctuation, middle-term period and long-term trend; inputting the preprocessed data into an encoder according to time sequence and static characteristics, taking a weighted average absolute error as a loss function, training a model according to a preset strategy, and outputting a final model; and finally, outputting a prediction result according to the final model, selecting a future preset time period price as a basic pricing, and carrying out dynamic pricing in combination with inventory feedback and region and category adjustment. According to the invention, the problem of low accuracy of poultry egg price prediction in the prior art can be solved.

The embodiment of the invention provides an electricity price prediction method and system based on LSTM and Transform, and belongs to the technical field of electricity price prediction. The electricity price prediction method comprises the following steps: acquiring multi-source historical data of a power system; performing type integration division on the multi-source historical data to obtain cost time sequence data, meteorological time sequence data and economic index time sequence data; preprocessing the cost time series data, the meteorological time series data and the economic index time series data; constructing a training set according to any two items in the cost time sequence data, the meteorological time sequence data and the economic index time sequence data to obtain three groups of cross training sets; a mode of constructing a training set by crossing multiple kinds of time series data and acquiring a real-time prediction sensitivity coefficient of each group of models is adopted, so that the influence weight of the time series data / models with a large electricity price influence degree can be effectively enhanced, and the prediction precision is improved.

The invention discloses an air ticket booking system and method based on a large language model, and the system comprises a data collection module which is used for collecting the current natural language query, historical dialogue records, real-time position information, historical behavior data and user portrait tags of a user after the authorization of the user is obtained; the query parameter structured processing module is used for carrying out space-time semantic analysis processing on the acquired data of the data acquisition module and outputting structured query parameters containing a departure place identifier, a destination identifier and departure time information; and the intention recognition module is used for performing intention classification reasoning through a pre-trained large language model based on the current natural language query, the historical dialogue record and the structured query parameters of the user, and outputting a fine-grained intention category comprising at least one of air ticket query, special-price air tickets, multi-day air tickets, price prediction and price reduction reminding. According to the system and the method, the query accuracy, the service suitability and the interaction efficiency are improved.

The invention discloses an urban residence land price evaluation method considering data imbalance and spatial heterogeneity. The method comprises the steps of collecting historical residence land sample data of a research area and performing spatial position matching; performing hierarchical index evaluation processing on the historical residential land sample data by using a multi-hierarchical index system, and performing fusion arrangement to obtain a residential hierarchical index land price data set; the land price evaluation combination model carries out model training of residence land price prediction by using a residence level index land price data set, and a differential evolution algorithm model DE dynamically searches a hyper-parameter combination of an XGBoost model and carries out optimization processing; and obtaining residential land multi-source data of the research block to obtain indexes of all levels, inputting the indexes into the land price evaluation combination model, and outputting and obtaining a residential land price evaluation prediction result. According to the method, the double problems of unbalanced sample price data and insufficient spatial heterogeneity identification are solved, high-precision urban residential land price evaluation is realized, and the accuracy and automation level of land price evaluation are improved.

Provided are a method for regulating a wind-photovoltaic-storage power station based on electricity, green certificate, and carbon price prediction, a device, a medium, and a product. The method includes: inputting acquired historical price data into a price prediction model, and outputting a predicted price; determining a deviation vector of price data based on the historical price data and the predicted price, and generating an uncertainty set of the predicted price by using a multi-kernel-based one-class support vector machine algorithm; classifying the uncertainty set of the predicted price by using a neural network classifier, to obtain multiple types of price scenarios; solving, based on predicted prices under the multiple types of price scenarios, a joint clearing model by using a Pied Kingfisher Optimization (PKO) algorithm, to obtain an operation strategy for the wind-photovoltaic-storage power station; and regulating the wind-photovoltaic-storage power station based on the operation strategy for the wind-photovoltaic-storage power station.

The application discloses a kind of based on large language model multi-agent cooperation's key raw material procurement decision optimization method, system, equipment and medium, it is related to supply chain intelligent decision, procurement optimization, big model application and multi-agent cooperation technical field, including the following steps: acquisition key raw material procurement related multi-source data;Multi-source data are standardized and time caliber alignment, generate uniform purchase state vector;Build purchase influence factor causal diagram and generate price prediction result package;Generate candidate procurement strategy task;Generate candidate procurement plan;Risk verification is carried out to candidate procurement plan;Determine execution procurement plan and reference procurement plan.The application adopts above-mentioned one based on large language model multi-agent cooperation's key raw material procurement decision optimization method, system, equipment and medium, through "observation-decision-optimization-feedback" closed-loop cooperation mechanism, improve the dynamic adaptability, explainability and execution feasibility of key raw material procurement decision.

The invention provides an original real-time electricity price prediction method based on a quantum complex neural network and Hilbert-Huang Transform (HHT), and the real-time electricity price prediction method based on the quantum complex neural network and the Hilbert-Huang Transform (HHT). Aiming at the non-stationarity of electricity price data on the historical level, firstly, the time internal correlation of each feature channel is extracted through an HHT time sequence analysis method, and complex multi-dimensional time sequence prediction is simplified into a simple regression task, so that a tedious time sequence modeling process is avoided; for the non-linear problem of electricity price data, a quantum neural network is used for capturing the coupling relation between different factors, and the calculation speed and the model efficiency are further improved by means of the parallelism of quantum calculation. Based on the two advantages, the method can realize accurate real-time electricity price prediction.

A machine wine price prediction method based on a federated learning architecture comprises the following steps: firstly, constructing a system comprising a data source module, a prediction module, a federated learning module and a price prediction module, determining aviation, hotel and online travel platforms as participants, and collecting and dividing data; after preprocessing, privacy matching and sample alignment, data isomerism is processed by the prediction module, a knowledge distillation mechanism and a confrontation framework are established by the federal module, and a three-layer decision tree is established by the price module. And each participant locally trains the model, the federal module aggregates parameters and iterates to converge, and finally, the price is predicted in combination with real-time data for reference. According to the method, the problems that data privacy protection is insufficient, heterogeneous and time-lag data are difficult to fuse, pricing compliance is insufficient and a model is easy to be attacked by poison in machine wine price prediction are solved, and accurate compliance pricing under multi-party privacy cooperation is realized.

The invention discloses a price monitoring method and system for preventing price discrimination, and belongs to the technical field of Internet. According to the invention, a shopping demand of a target user is acquired, a price attention group is determined according to the shopping demand, and the price attention group comprises article information of a target article and a target user who purchases the target article; recording price discussion data of the price attention group, and determining a price data set according to the price discussion data and the price monitoring data; predicting a predicted price of the target article according to the price data set and a preset price prediction model; and generating a purchase suggestion of the target article according to the predicted price, the price data set and the price information at the current moment. The method has the beneficial effect of reducing the probability of selection and purchase by the user at an unfair price.

The application discloses a kind of power spot market electricity price prediction model establishment method, it is related to electric power technical field.The application is by collecting the time-sharing electricity price of day-ahead market, real-time market;Utilize similar day to fill in missing value, isolated forest algorithm detects abnormal value;ARIMA data model is constructed, and periodicity is captured Price;Reasonable correction is carried out to negative electricity price prediction value, and prediction output is adjusted in combination with price upper limit, and probability prediction form provides prediction interval;Setting prediction error early warning threshold, model is re-estimated every week, and the adaptability of model to market change is maintained;The present application is based on the mature theoretical framework of time series analysis, parameter has clear mathematical explanation, and complete "white box" model, prediction process is explainable, each component can be separated and analyzed, non-stationarity of electricity price sequence is effectively eliminated by difference processing (d parameter), can adapt to the trend change and seasonal fluctuation of electric power market price, especially good at modeling autocorrelation characteristics of electricity price.

The invention provides an anchor point selection method and system for asset price prediction. The method comprises the following steps: receiving historical price data of target assets and candidate assets in a predetermined period; determining the correlation between the candidate assets and the target assets based on the historical price data of the target assets and the candidate assets; determining the sensitivity of the target asset to the price fluctuation of the candidate assets for the determined candidate assets with high correlation; and taking the determined candidate assets with high sensitivity as anchor assets, so as to predict the asset price of the target asset based on the anchor assets.

An electric power spot transaction decision support system based on multi-dimensional market analysis relates to the field of electric power spot transaction, and comprises a display layer which supports complex interaction through a Web end, large-screen cockpit command center monitoring and small program mobile end access, and realizes interaction result multi-terminal output. The application layer is communicated with the display layer, comprises a market analysis module and is used for processing multi-dimensional external market data to generate standardized background data; a user side load prediction module generates a user load day-ahead prediction result through an ARIMAX model based on historical power consumption and external factors, predicts monthly power by combining multiple linear regression and other models, and dynamically allocates weights by adopting an error reciprocal normalization method; and the day-ahead price prediction module cleans and preprocesses data such as historical electricity prices, and outputs a result through multi-model price comparison evaluation. The bottom layer is integrated with a scheduling management system through a data interaction interface, is in butt joint with a power grid operation environment and supports efficient decision-making of energy business.

The invention provides an electricity-carbon-green certificate multi-mode dynamic coupling pricing and cross-chain clearing method based on a federated learning-block chain fusion framework. Data islands are broken through differential privacy horizontal federation aggregation, an LSTM-GRU hybrid model is trained, and global price prediction with the error smaller than 5% is achieved; a TD3 reinforcement learning agent is introduced, a nonlinear linkage coefficient is generated in real time, the policy mutation response speed is increased by 50%, and the arbitrage space is compressed by 40%; a private chain and alliance chain double-chain heterogeneous architecture is constructed, an HTLC is used for binding electricity purchasing, carbon selling and green certificate purchasing as an inseparable transaction, the clearing delay is smaller than or equal to 800 ms, and the failure rate is smaller than 0.01%; zK-SNARK zero-knowledge proof is adopted to ensure that transactions can be audited and privacy is not leaked. The provincial 100-node cluster actual measurement shows that the prediction error is reduced by 75%, the policy delay is shortened by 51%, the combined transaction failure rate is reduced by 99.9%, and safe, efficient and compliant technical support is provided for multi-market collaboration in a high-fluctuation environment.

The invention discloses an electricity price prediction method and system, and relates to the technical field of electricity price prediction, and the method comprises the steps: collecting the historical electricity price, power load, meteorological data and text data of an electricity market, and carrying out the normalization processing of the historical electricity price data, power load data and meteorological data, and forming multi-dimensional data; extracting time sequence features of the multi-dimensional data by using a convolutional neural network, and extracting text features of the text data by using a large language model; carrying out feature fusion by utilizing a large language model to generate predicted electricity price and case interpretation; and carrying out iterative optimization by using an inverse LLM (Language Language Model). According to the method, multi-modal influence factors such as power load, weather conditions and policy reports are fully considered, and a new technical thought is provided for electricity price prediction.

The invention provides a new energy equipment price prediction method based on an incremental knowledge graph. The new energy equipment price prediction method comprises the steps of constructing a four-dimensional dynamic knowledge graph, fusing multi-source data, determining nodes to be updated of the four-dimensional dynamic knowledge graph through a quantum optimization algorithm, and extracting spatio-temporal features in combination with time sequence modeling to update the four-dimensional dynamic knowledge graph. The method comprises the following steps: hierarchically configuring an update strategy based on event change frequency, constructing a key event influence model, predicting an event occurrence rule in combination with a long-short-term memory network model to drive a four-dimensional dynamic knowledge graph to update, and finally, inputting four-dimensional dynamic knowledge graph features into a machine learning prediction model to predict the price of new energy equipment. The problems that a traditional prediction model is low in updating efficiency and poor in timeliness are solved, and the price prediction accuracy and efficiency are improved.

The invention relates to a carbon quota transaction strategy determination method and device, a medium, equipment and a program product, and the method comprises the steps: building an electric power balance constraint, simulating the carbon emission of an electric power system, constructing a carbon emission reduction marginal cost curve, and determining the thermal power cost carbon price of a next period according to the carbon emission reduction marginal cost curve; calculating a balanced carbon quota price by taking the target interference yield of each object as a reference; the predicted carbon quota price of the next period is predicted through the carbon quota price prediction model, the predicted carbon quota price is obtained through prediction from the perspective of carbon market transaction, and after the carbon quota transaction data are subjected to data segmentation according to different segmentation time windows, the carbon quota transaction data are subjected to data segmentation; performing data splicing according to a plurality of splicing time windows of different dimensions to obtain training data of a carbon quota price prediction model; and determining a carbon quota trading strategy according to the mutual relation among the actual trading carbon price, the thermal power cost carbon price, the balanced carbon quota price and the predicted carbon quota price.