179 results about "Electricity price forecasting" patented technology

The multi-energy complementary heat supply system comprises an external basic heat source, a middle-grade synergistic heat source, a high-grade energy storage heat source, an intelligent heating power mixing center and a central controller, and the intelligent heating power mixing center is used for heating heat source equipment of all levels and operation instructions of associated assemblies according to the operation instructions of the heat source equipment of all the levels and the operation instructions of the associated assemblies. Heat energy of different grades is mixed and allocated, and heat loads meeting the grade requirements of heat consumers are output to the heat consumers; the central controller is used for establishing a multi-target optimization model taking the minimum total operation cost of the system as a core target based on the electricity price prediction data and the different-grade thermal load prediction data, solving the multi-target optimization model and generating a system operation strategy under the condition that preset basic guarantee logic, economic operation logic and peak response and power grid interaction logic are met; and the generated system operation strategy is converted into an operation instruction for the heat source equipment of each level and the associated assembly, so that the reliability and the adaptability of operation control of the heat supply system are effectively improved.

The invention discloses a photovoltaic energy storage cooperative control method and system, and the method comprises the steps: obtaining the real-time state data and environment information of distributed photovoltaic and energy storage equipment, and constructing a system network topology; constructing a hierarchical prediction model including photovoltaic power generation prediction, load demand prediction and electricity price prediction based on the system state data set; constructing a multi-objective function containing an economic benefit objective and a system stability objective based on a prediction result, and performing objective decomposition through recursive iteration; according to a local optimization target, generating a photovoltaic power adjustment strategy and an energy storage charging and discharging strategy by reducing a distributed maximum matching gap and reducing an order to break a distributed maximum independent set obstacle algorithm; and based on the distributed control strategy set, the system operation state is monitored in real time, and control parameters are dynamically adjusted by adopting an event triggering mechanism. According to the method, the problems of calculation complexity and communication dependence of traditional centralized control in a large-scale photovoltaic energy storage system are solved.

Provided is a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, which method belongs to the technical field of day-ahead electricity price prediction in a power market. The maximum mutual information coefficient is used to select variables having a certain relevance with an electricity price, so as to assist in predicting future electricity price data; next, a decomposition algorithm is used to decompose an original electricity price signal; and then, electricity price sub-components obtained by means of decomposition and the electricity-price-related variables are inputted into an MBI-IPMDA-PBISA deep learning model, so as to predict a future electricity price. In this way, an MBI-IPMDA-PBISA deep learning model can fully learn a complex association relationship between electricity price sub-components and electricity-price-related variables and a future electricity price, the problem of error superimposition caused by separately predicting the electricity price sub-components and then superimposing same to obtain the future electricity price is also avoided, and the problem of the accuracy of existing attention mechanisms during prediction being low is solved, thereby improving the prediction accuracy.

The invention discloses a distributed photovoltaic power distribution network shared energy storage optimization method, and relates to the technical field of energy storage system optimization, and the method comprises the following steps: inputting the current electricity price of an electricity market into a preset electricity price prediction model, and obtaining a current rolling cycle electricity price prediction sequence, constructing a rolling optimization model based on the current rolling cycle electricity price prediction sequence, the energy storage system battery real-time charge state and the energy storage system operation constraint condition, taking the maximum energy storage system net income as an objective function, taking the battery life attenuation cost as a correction parameter, and obtaining an optimal charge and discharge power sequence, and controlling the charging and discharging actions of the energy storage system, monitoring an electricity price prediction deviation index in real time, optimizing and calibrating an electricity price prediction model, updating an optimal charging and discharging power sequence, adjusting the charging and discharging actions when the electricity price prediction deviation index is greater than a preset deviation threshold value, and repeating the steps at the beginning of a next rolling period, so that the benefits of the energy storage system are improved, and the battery loss is reduced.

The invention discloses a household electrical load optimization scheduling method and system based on an MAPPO algorithm. The method comprises the following steps: (1) collecting multi-source heterogeneous data from a user side and an external environment and carrying out preprocessing; (2) estimating the future equipment use probability of the user by collecting historical equipment use data of the user, and outputting an equipment behavior prediction vector; (3) acquiring real-time electricity price information, performing short-term electricity price trend prediction by using the time sequence prediction model based on a historical electricity price sequence, and outputting an electricity price prediction sequence; (4) generating a scheduling strategy by the MAPPO network based on the equipment behavior prediction vector and the electricity price prediction sequence, and outputting a corresponding scheduling result; and (5) sending a scheduling result to the home gateway, controlling the equipment, collecting an execution feedback result in real time, updating internal parameters of the MAPPO network, and realizing strategy iteration and adaptive adjustment of the MAPPO network.

The invention relates to the technical field of computers, and discloses an intelligent scheduling system and method for a high-efficiency charging platform, and the method comprises the steps: constructing a four-dimensional constraint model integrating the power grid load, the user demand, the equipment health degree and electricity price prediction, and employing a depth deterministic strategy gradient algorithm to drive a multi-target dynamic scheduling decision maker, the global optimization distribution of the charging resources in the space-time power dimension is realized, and strategy reconstruction is completed within 10s when a power grid emergency instruction or a device fault occurs. The system comprises a power grid sensing module, a user acquisition module, a health assessment module, an electricity price response module, a scheduling decision module, an instruction execution module and an emergency reconstruction module, and supports millisecond-level adaptive evolution. According to the method, the weighted reward function is constructed by quantifying the four indexes of power grid stability, user satisfaction, equipment loss and platform income, and the instruction verification and steady-state adaptive mechanism is combined, so that the user experience is synchronously improved, the service life of the equipment is prolonged, and the operation energy consumption is reduced on the premise of ensuring the safety.

The embodiment of the invention provides an energy storage charging and discharging strategy optimization method and device based on day-ahead and real-time electricity price fluctuation, and the method comprises the steps: obtaining a day-ahead electricity price prediction matrix which comprises the day-ahead electricity price prediction value of each time period in an energy storage system, and constructing a day-ahead income objective function through the day-ahead electricity price prediction matrix; generating a day-ahead charging and discharging declaration strategy for reflecting the state of the energy storage system in each time period by using the day-ahead income objective function; after entering the real-time market, acquiring a real-time predicted electricity price matrix containing the real-time predicted electricity value of each time period in the energy storage system, and constructing a real-time income objective function by using the real-time predicted electricity price matrix; a result of maximizing the revenue in the real-time market by considering the conditional value-at-risk, and establishing a final revenue model by taking the sum of the day-ahead revenue and the real-time revenue as a target; and performing optimal energy storage charging and discharging strategy optimization solution on the final income model to obtain an optimal energy storage charging and discharging strategy.

The invention discloses a new energy electricity price accurate prediction method and system based on virtual power plant aggregation regulation and control, and relates to the technical field of industrial data processing. The method comprises the steps: S1, carrying out the preprocessing of meteorological environment data, output equipment data and battery power grid operation data; s2, constructing a variation dynamics analysis and mutation depth discrimination mechanism based on the meteorological environment data, and entering an output prediction and optimization process and a scheduling load response process in a layered manner; s3, executing energy storage collaborative optimization scheduling and parameter correction according to a meteorological output regression prediction analysis result; and S4, performing electricity price time sequence multi-source driving analysis, and reconstructing an electricity price fluctuation response strategy. The problems that new energy output violently fluctuates within a minute level due to sudden weather change, and electricity price prediction lags due to the fact that an existing prediction model depends on too low weather data updating frequency and cannot capture the rapid change in time are solved.

The invention belongs to the technical field of electricity price prediction, and particularly discloses an electricity price prediction method and system based on dynamic subgraph learning, a terminal and a medium. Comprising the steps of collecting multi-source electricity market data such as load, weather and market transaction, performing normalization and missing value filling, and constructing a dynamic electricity price information graph; dynamic sub-graph division is executed based on the edge weight calculated in real time among the nodes, and a density peak value clustering method is adopted to determine the center of the sub-graph and periodically update the center of the sub-graph; extracting spatial features in the sub-graph through a graph convolutional network, generating a sub-graph embedded vector, and inputting the sub-graph embedded vector into a bidirectional recurrent neural network to obtain time sequence features; utilizing a multi-head attention mechanism to realize interactive fusion among different sub-graphs to obtain global feature representation; and predicting the future electricity price in combination with the global features and the historical electricity price sequence. And in the face of new energy output fluctuation, load sudden change or market mechanism adjustment and the like, the prediction flexibility and accuracy are improved.

The invention relates to an electrolytic hydrogen production system operation strategy adjusting method and device, equipment and a storage medium. The method comprises the steps of predicting a node electricity price of a power system based on obtained load prediction data, renewable energy output prediction data, unit operation parameters, power grid topology data and a constructed electricity price prediction model, and obtaining an electricity price prediction result; based on the electricity price prediction result, the obtained hydrogen demand data and operation data of the electrolytic hydrogen production system, the constructed electrolytic hydrogen production system model and a preset electric energy balance constraint condition, a preset optimization objective function is solved with the goal of minimizing the hydrogen production cost, and the optimal hydrogen production cost is obtained. Obtaining a target start-stop plan and a target operation power plan of the electrolytic hydrogen production system, wherein the optimization target function is constructed based on the operation cost of the power system and the operation cost of the electrolytic hydrogen production system; and adjusting the operation strategy of the electrolytic hydrogen production system based on the target start-stop plan and the target operation power plan. The method is beneficial to reducing the consumption of hydrogen production resources.

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 discloses a day-ahead electricity price prediction method and system based on similar day adaptive screening and SHAP compensation. The method mainly comprises the following steps: preprocessing historical electricity price, load, new energy output and meteorological data; calculating the maximum information coefficient of the features and the electricity price based on a sliding window, and carrying out the self-adaptive screening of similar days through combining the clustering and grey correlation degree after dynamic weighting; the method comprises the following steps: constructing a prediction model by fusing a conditional variation auto-encoder with a multi-head attention mechanism, capturing historical sequence multi-scale time sequence characteristics and future condition information through a double-path encoder, and generating a day-ahead electricity price point prediction result; and performing feature contribution decomposition on the prediction error by using an SHAP tool, and training an error compensation model to correct an initial prediction value. The method can dynamically adapt to market changes, the prediction precision and interpretability are improved, closed-loop self-optimization is achieved, and reliable decision support is provided for electricity market transactions.

The invention discloses an electricity price prediction method and system based on a converter and a bidirectional gating cycle network, and belongs to the technical field of power systems and artificial intelligence prediction.The method comprises the steps that a historical electricity price data set is acquired, the historical data set is constructed, and the historical data set is preprocessed; the preprocessed data is input into a Transform encoder layer in a hierarchical multi-head attention mechanism; a bidirectional gating recurrent neural network optimized through a global attention mechanism; a cross attention mechanism is combined with the output of a Transform encoder and the output of a BiGRU layer; and performing performance evaluation on a prediction result by adopting an absolute mean error, a mean square error and a root-mean-square error. According to the invention, while the time sequence dynamic modeling capability is maintained, the physical constraint information of the power system is effectively integrated, and the generalization capability of the electricity price prediction model for the multi-source uncertainty in the high-proportion renewable energy penetration scene is significantly 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 energy storage system resource scheduling method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-scale time sequence analysis of electricity price time sequence data, obtaining candidate time sequence data, inputting the candidate time sequence data, power grid load data and weather characteristic data into an electricity price prediction model, and outputting the electricity price prediction information, an electricity price income optimization objective function and constraint conditions of the energy storage system are constructed based on the parameters of the energy storage system, a resource scheduling model is constructed based on the electricity price predicted value, the electricity price income optimization objective function and the constraint conditions, and the energy storage system is controlled to perform resource scheduling based on a target resource scheduling strategy output by the resource scheduling model; by accurately capturing the multi-scale characteristics of the electricity price data, the multi-constraint coupling problem of the energy storage system is effectively optimized, the electricity price prediction precision is greatly improved, the resource scheduling strategy of the energy storage system is effectively optimized, the loss cost of the energy storage system is reduced, and the resource benefits of the energy storage system are improved.

The invention discloses an electricity price prediction method based on dynamic mode decomposition fusion LSTM + CKDE. The method comprises the following steps of: 1, collecting data, including historical electricity prices containing time sequence characteristics and price fluctuation indexes, power generation parameters, power market supply and demand data and external environment factor parameters; 2, performing data preprocessing, abnormal value processing and missing value interpolation on the collected data; 3, performing feature input variable screening on the data processed in the step 2 by adopting a maximum information coefficient method or a Pearson correlation coefficient analysis method; 4, performing dynamic modal decomposition on the screened feature input variables to obtain dynamic feature vectors; step 5, constructing an electricity price prediction model by using an LSTM algorithm, and inputting a dynamic feature vector; and 6, evaluating the electricity price prediction model by adopting a CKDE method. According to the invention, the accuracy of electricity price prediction is improved.

The invention belongs to the technical field of electricity price prediction, and particularly relates to a micro-grid electricity price prediction method and device based on multivariate characteristic decomposition learning, and the method comprises the steps: evaluating the correlation between external influence factors and electricity price, and selecting input characteristics; performing local abnormal factor detection, cubic spline interpolation filling and standard normalization processing on the selected input features, and integrating the selected input features into a unified multi-dimensional input tensor; performing feature extraction on the input multi-dimensional input tensor; taking output of the multi-scale dynamic convolution architecture as input, and performing attention enhancement from two dimensions of time and channel; taking the output of the enhanced space-time attention mechanism as the input of a GRU network, and outputting an electricity price prediction value by using the GRU network; and carrying out model training by adopting an Adam optimizer, and carrying out final electricity price prediction by utilizing the trained model. According to the method, multivariable characteristic decomposition and deep learning optimization are fused, and the micro-grid electricity price prediction precision and efficiency are remarkably improved.

The invention provides an energy storage system charging and discharging method and device based on stochastic programming, and relates to the technical field of energy storage, and the method comprises the steps: obtaining electricity price market data and energy storage system state data; and training an electricity price prediction model based on the electricity price market data, and generating electricity price probability distribution corresponding to each time point in the planning period by using the trained electricity price prediction model. And adopting a Monte Carlo simulation method to perform random sampling on the electricity price probability distribution corresponding to each time point in the planning period, and generating an electricity price scene set comprising a plurality of electricity price scene paths. Based on the electricity price scene set and the energy storage system state data, constructing a stochastic programming model including a target function and constraint conditions; the objective function comprises a risk measurement item, a battery aging cost item and an expected total income item under all electricity price scene paths. And solving the stochastic programming model to obtain a charging and discharging strategy of the energy storage system at the current time point. According to the method, the benefit, risk and cost of the energy storage system are collaboratively optimized.

The invention discloses an electric power spot market energy storage charging and discharging decision-making system based on machine learning, and the system comprises a data collection module which is used for obtaining multi-source data of an electric power spot market, the multi-source data at least comprises power transaction data, weather related data, energy price data, supply and demand data, renewable energy output data, energy storage system parameters, energy structure characteristics and carbon emission related data; and the electricity price prediction module analyzes the multi-source data acquired by the data acquisition module on the basis of a deep learning algorithm in combination with an attention mechanism so as to realize electricity price prediction of three time scales, i.e., day-ahead, day-in-day and real-time, of the electric power spot market. The multi-time-scale electricity price prediction accuracy of the electric power spot market is not lower than 88%, the load prediction accuracy is not lower than 92%, and market price fluctuation and load change rules are accurately captured.

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 relates to the field of transaction electricity price prediction, in particular to an electricity spot market transaction electricity price prediction method. According to the invention, on the basis of the power capacity data of the power spot market, the historical accidental outage capacity of each unit time period in each historical day is obtained and the periodicity is analyzed, and the total estimated available capacity is determined by combining the environmental parameters of each unit time period of the prediction day and the total load analysis accidental outage capacity of the system. Calculating a spinning reserve rate and obtaining a corresponding initial electricity price in combination with the total system load of each unit time period of the prediction day, constructing a temperature trajectory vector of each unit time period of each historical day according to the sensible temperature of the same month over the years of the prediction day, and counting the electricity price change rates corresponding to different temperature trajectory vectors; according to the sensible temperature variable quantity of each unit time period of the prediction day, all the time periods to be corrected are screened, the initial electricity price is corrected according to the electricity price change rate corresponding to the temperature trajectory vector of each time period to be corrected, and the accuracy of electricity price prediction in a complex environment is improved.

The invention discloses a virtual power plant spot transaction and dynamic adjustment method based on a multi-element flexible load, and the method comprises the steps: 1, taking an electricity price prediction value in future H hours, a current energy storage charge state, a reducible load, a transferable load, and a transferable load as states based on a Markov decision process, and carrying out the dynamic adjustment of the virtual power plant spot transaction; establishing a multi-element flexible load response model by taking the discretization scheduling instruction combination as an action; step 2, according to the multi-element flexible load response model, calculating a discretization scheduling instruction combination corresponding to a current electricity price prediction value; according to the method, a state-action-reward framework is constructed, the physical constraint of the multi-element flexible load is embedded into a reinforcement learning process, adaptive, high-income and feasible transaction strategy optimization is realized, and the economic benefit of a virtual power plant in a spot market and the operation stability of a power system are improved.

The application discloses a kind of day-ahead electricity price prediction method, system, equipment and storage medium of fusion physical state identification, relating to power market electricity price prediction technical field, including based on power market clearing mechanism, construct physical state variable, define the physical feasible region of price under specific operating state through multivariate kernel density estimation, provide state constraint for deep learning model;Physical perception loss function is constructed, and is embedded in the training process of deep learning model, guide the price sensitivity of deep learning model under different operating states is differentially modeled;Using the trained deep learning model, combined with the price distribution characteristics under physical state condition, the prediction sample deviating from the physical feasible region is constrained and corrected, and the day-ahead electricity price prediction result is obtained.The method disclosed in the application improves the physical consistency and explainability of the prediction result, strengthens the modeling ability of the model to extreme price intervals such as peak electricity price and negative electricity price, and improves the stability of the model.

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.

This application discloses a method, apparatus, equipment, and medium for day-ahead nodal electricity price forecasting and correction based on price state grouping and gating correction, relating to the field of electricity price forecasting technology. The method includes: constructing a training sample window using historical market operation characteristic data; classifying price states according to historical clearing prices to generate price state labels; training a model within the training sample window to obtain a global forecasting model and a price state sub-forecasting model; performing offline initial forecasting of day-ahead nodal electricity prices using the global forecasting model, and correcting the initial price forecasting result by calling the corresponding sub-forecasting model according to the corresponding price state; if a forecast value meets a preset gating condition, further determining whether it meets a preset zero-price condition; if so, performing gating correction to obtain the final electricity price forecasting result. This application can improve the targeting and accuracy of day-ahead nodal electricity price forecasting under different price states, and enhance the ability to identify and correct special price states such as zero-price.

The invention provides an electricity price prediction method and device based on multi-modal data fusion and a space-time diagram neural network, equipment and a medium, and relates to the technical field of electric power. The method comprises the following steps: acquiring historical electricity price sequences and electricity price association characteristics of a plurality of preset electricity transaction areas; performing multi-component time sequence signal decomposition on the historical electricity price sequence, inputting the historical electricity price sequence into a preset TCN sub-network, and outputting a time sequence feature vector of each electricity transaction area; splicing the time sequence feature vector and the electricity price association feature of each electricity transaction region, inputting the spliced time sequence feature vector and the spliced electricity price association feature as node features into a preset GCN sub-network, and outputting a spatial feature vector of each electricity transaction region in combination with an adjacent matrix constructed by the inter-region power exchange capacity and the geographic distance; and carrying out element-by-element addition fusion on the time sequence feature vector and the spatial feature vector of each electricity transaction area, inputting the fused data to a full connection layer, and outputting a predicted electricity price sequence of each electricity transaction area. The accuracy of electricity price prediction can be improved.

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.

The invention relates to the technical field of electricity market prediction, in particular to an electricity market day-ahead electricity price prediction method and system based on an enhanced linear Stacking framework, and the method specifically comprises the steps: obtaining multi-source heterogeneous data, carrying out the preprocessing of the data, obtaining a standardized feature matrix, calculating the feature importance through a random forest, and carrying out the calculation of the feature importance; k-fold time sequence cross validation and marginal benefit analysis are combined to screen an optimal feature subset, two basic learners are constructed, fold time sequence cross validation is adopted to train the basic learners, fold-out prediction vectors are obtained, statistical features are extracted, and the fold-out prediction vectors and the statistical features serve as input to train the basic learners, so that the optimal feature set is obtained. And then the optimal hyper-parameter of the basic learner is searched through Bayesian optimization and re-fitting is completed, the meta learner is trained to obtain a Stacking integrated model, a test sample is input into the model, and a day-ahead electricity price prediction value of an original dimension is obtained through inverse transformation. The method can improve the accuracy and stability of electricity price prediction, and is suitable for an electricity market day-ahead electricity price prediction scene.

The invention relates to the technical field of day-ahead electricity price prediction, in particular to a day-ahead electricity price prediction method, device and equipment based on a diffusion model and a computer readable storage medium, and the method comprises the steps: obtaining day-ahead market transaction data, real-time transaction data and auxiliary information in a fixed time period every day, and writing the data into a market information database; based on the market information database, aligning the market information database according to unified time granularity to obtain a multi-dimensional condition feature sequence; inputting the multi-dimensional condition feature sequence into the trained de-noising diffusion probability model, and generating an electricity price prediction sample set at discrete time points in the next day through reverse multi-step Markov de-noising sampling; and obtaining a statistical value of the electricity price prediction sample set at each time point, constructing a prediction curve, taking a preset quantile to obtain a probability interval, and writing a result into prediction data and an evaluation database. Probabilistic generation is carried out on the day-ahead electricity price by adopting a diffusion model, and the prediction precision in an extreme fluctuation scene is remarkably improved.

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.