271 results about "Electricity price forecasting" patented technology

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention relates to the technical field of uninterruptible power supplies, and provides a UPS peak shifting charging method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining electricity price historical data and load historical data in historical data of a power grid; performing model training on the electricity price historical data and the load historical data to obtain electricity price prediction data and load prediction data; determining a preset charging strategy of the UPS system based on the electricity price prediction data and the load prediction data; determining a real-time charging demand according to the obtained real-time battery pack capacity and the real-time load data of the power grid; the preset charging strategy is updated according to the real-time charging requirement, the target charging strategy is obtained, and the charging strategy of the UPS system is dynamically adjusted according to the real-time load condition of the power grid.

The invention provides a new energy station digital intelligent operation system, a control method, equipment and a medium, and relates to the technical field of new energy. According to the system, a marketization electricity price prediction result and a new energy output prediction result are output through an electricity price prediction module and a new energy output prediction module respectively, and an energy storage optimization scheduling module and a planning layout decision module work cooperatively. And combining the marketization electricity price prediction result and the new energy output prediction result to realize operation strategy optimization and capacity configuration decision of the new energy station, and solving and outputting a capacity configuration scheme corresponding to the new energy device and the energy storage device. According to the technical scheme, the multi-module collaborative new energy station digital intelligent operation system is constructed, so that full-link closed-loop optimization from prediction to scheduling to planning is realized, the adaptability of the system to power market fluctuation can be remarkably enhanced, and the operation income of the new energy station is ensured.

The invention provides a prediction method and device for operation parameters of a virtual power plant, a storage medium and a computer program product. Comprising the steps of performing data preprocessing on acquired influence factor data; using a time sequence decomposition algorithm to decompose the influence factor data into three-dimensional sequences including a trend component, a season component and a residual component, and segmenting each sequence into sequence blocks and mapping the sequence blocks into time sequence feature vectors; performing K-means clustering on word embedding used for pre-training the large language model, and selecting K clustering centers as semantic anchor points to be spliced with the time sequence feature vectors; finely adjusting the position embedding parameters of the large language model, the weight of the feedforward neural network in residual connection and the parameters of the normalization layer; and according to the semantic enhancement time sequence feature vector, predicting to obtain a trend component, a season component and a residual component of each dimension, and carrying out splicing and reverse normalization processing on the components to obtain a prediction result. According to the prediction method, the accuracy and real-time performance of virtual power plant load and electricity price prediction are improved.

The invention discloses an electricity price prediction large model construction method and system based on federated learning, and relates to the related technical field of electricity price prediction, and the method comprises the steps: executing the data collection of a time window in a preset time window, and building a window data set; executing sample feature extraction; performing multi-dimensional feature clustering by using the multi-dimensional feature identifier and configuring N clusters; performing cluster division based on double identification channels, and establishing an independent cluster and a combined cluster; independently training the decision tree by using the independent cluster, and executing weak classifier mapping training of the combined cluster; and establishing an electricity price prediction large model. The technical problems that in the prior art, power industry data are difficult to share, multi-source heterogeneous data cannot be integrated through federal learning, the electricity price prediction precision is low, and the prediction model generalization ability is poor are solved, and the technical effects that multi-mechanism data collaborative modeling is achieved, and the electricity price prediction model generalization ability and the electricity price prediction precision are enhanced are achieved.

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 relates to the technical field of electric power market price prediction, in particular to an electric power spot price prediction method and system based on similar day probability correction, and the method comprises the following steps: S1, electric power data collection: obtaining historical market information of a day-ahead spot market and day-ahead electric power spot price data; s2, feature engineering construction: performing feature engineering construction on the collected power data; s3, data preprocessing: forming a data set; s4, performing model training: performing training on the LightGBM model; s5, similar day matching: screening k historical days with the highest similarity with the prediction day; s6, performing interval probability distribution modeling of the bidding space and the day-ahead electricity price: establishing a probability mapping matrix of the bidding space interval and the day-ahead electricity price interval; and S7, prediction result correction and final electricity price prediction: generating a final day-ahead spot electricity price prediction sequence. According to the invention, the method is closer to the actual market situation when the prediction logic is constructed, and the stability and adaptability of electricity price trend modeling are enhanced.

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.

The invention provides a new energy station energy storage control system and method, and the system comprises a new energy output high-precision prediction module which is used for fusing local laser radar and numerical weather forecast data, and outputting an output predicted value; the SOC curve initialization module is used for initializing an energy storage SOC reference curve based on the output predicted value and the day-ahead market electricity price information; the real-time node electricity price prediction module is used for predicting a real-time electricity price trend; and the rolling optimization control module of the energy storage system is connected with other modules, and dynamically optimizes the charging and discharging instruction and the SOC state according to the output prediction value, the energy storage SOC reference curve and the electricity price prediction value of the real-time node electricity price prediction module. According to the invention, by fusing the multi-source prediction result and the real-time market information, on the premise of satisfying various operation and market constraint conditions, a highly adaptive energy storage dynamic optimization regulation and control mechanism is constructed, and key support is provided for economical operation of a new energy station in a spot market environment.

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 provides a new energy optimal power supply strategy method and system based on artificial intelligence, and relates to the technical field of new energy power supply strategies, and the method comprises the steps: constructing an electricity price prediction model, a weather prediction model and a human traffic-load prediction model; a dynamic scheduling optimization model is constructed according to the power supply cost, the power abandonment rate and the load deviation, and the power supply cost is calculated according to a prediction result of the electricity price prediction model; the electricity abandoning rate is determined according to a prediction result of the weather prediction model; the load deviation is calculated according to a prediction result of the pedestrian flow-load prediction model; and according to the dynamic scheduling optimization model, carrying out dynamic adjustment on economic, environmental protection and stability strategies of the power grid. The system comprises a data collection module, an electricity price prediction module, a weather prediction module, a people flow-load prediction module, a dynamic scheduling optimization module and a data output module. According to the invention, an efficient and intelligent solution can be provided for new energy optimal power supply strategy configuration.

The invention provides a new energy configuration, construction and energy storage combined operation optimization system and method, and relates to the technical field of new energy configuration and energy storage. According to electricity price prediction data, a single wind power station output prediction result, a single photovoltaic power station output prediction result, an AGC active power instruction and new energy real-time active power, overall income maximization is taken as a target function, new energy output constraint and energy storage operation cost are considered, and a new energy output prediction result is obtained according to a general principle of low-price charging and high-price discharging. And an energy storage charging and discharging strategy in a future period is given, the charging and discharging strategy is issued to the energy storage EMS, and automatic charging and discharging are controlled by the energy storage EMS. By predicting the node electricity price, the energy storage charging and discharging time is reasonably planned, the energy storage charging and discharging actions are automatically controlled, and the utilization rate of new energy distribution and storage is improved. By predicting the node electricity price, the energy storage charging and discharging time is reasonably planned, the energy storage charging and discharging actions are automatically controlled, and the utilization rate of new energy distribution and storage is improved.

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 relates to the technical field of electric power spot market real-time price prediction, and discloses an electric power spot market real-time price prediction method based on low-sample high-dimension data, and the method comprises the steps: extracting historical data from a database; carrying out feature extraction from historical data, and carrying out standardization processing on the extracted features; constructing a real-time electricity price prediction model by adopting a generative neural network model, and inputting the extracted features of the historical data into the real-time electricity price prediction model to obtain a first predicted electricity price; inputting the first predicted electricity price into a heuristic predictor for adjustment to obtain a second predicted electricity price; adjusting factors for adjusting the first predicted electricity price by the heuristic predictor comprise similarity, a trend factor, a seasonal factor and a special event adjusting factor, and fusing the adjusting factors to comprehensively adjust the first predicted electricity price; existing electricity price prediction cannot be reasonably quantified due to external factors in the early stage of development of an electric power spot market, so that the prediction result is inaccurate.

The invention discloses a regional electricity price prediction method, device and system, and a storage medium. The method comprises the following steps: S1, constructing a regional electricity price prediction function; s2, extracting regional electricity price data features; and S3, according to the regional electricity price prediction function and the regional electricity price data features, regional electricity price prediction is carried out through an LSTM deep learning structure. By adopting the technical scheme of the invention, the accuracy and real-time performance of electricity price prediction can be remarkably improved, a scientific basis and decision support are provided for participants of the electricity market, and the healthy development and sustainable development goal of the electricity market is promoted to be realized.

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 provides a method for predicting spot electricity price based on machine learning and an adaptive mechanism and computing equipment, and is applied to the technical field of electricity price prediction. The method comprises the following steps: acquiring a dynamic data set; calculating basic characteristics by using the dynamic data set, wherein the basic characteristics comprise a thermal power generating unit bidding space; constructing composite features based on the basic features to form a factor library; selecting features used for modeling in the factor library, calculating segmentation indexes of the electricity price in the first time, and finding out the bidding space of the thermal power generating unit corresponding to the demarcation electricity price; and independently establishing a prediction model for each segmented index, splicing data results of the segmented index prediction models, and performing spot electricity price prediction. By using the method of the invention, accuracy and reliability of electricity price prediction can be effectively increased.

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 based on long-term and short-term time series networks of an attention mechanism. The method comprises the following steps: acquiring a data set related to electricity price; calculating the correlation between the electricity price influence factors and the electricity price change in the data set so as to sequentially screen out the electricity price influence factors with relatively strong correlation, and constructing a sample set for the data set according to the screened electricity price influence factors and the electricity price; dividing the sample set into a training set and a test set, and normalizing data of the training set and the test set; forming an input matrix by a plurality of continuous time sequences in the normalized training set, inputting the input matrix into long-term and short-term time sequence network models based on an attention mechanism for training, and finely adjusting parameters in the training process to obtain a day-ahead electricity price prediction model with optimal parameter configuration as a trained day-ahead electricity price prediction model; and forming an input matrix by a plurality of continuous time sequences in the normalized test set, inputting the input matrix into the trained day-ahead electricity price prediction model for testing to obtain a predicted day-ahead electricity price data normalized value, and performing reverse normalization to obtain a day-ahead electricity price prediction value of a standard dimension. According to the invention, the precision of day-ahead electricity price prediction can be effectively improved.

The invention provides a small sample electricity market electricity price prediction method and system based on meta-learning, and belongs to the technical field of electricity market analysis based on artificial intelligence, and the method comprises the steps: obtaining the operation state data of an electric power system of a to-be-predicted region; and processing the acquired operation state data of the power system by using a pre-trained electricity price prediction model to obtain an electricity price prediction result. By constructing the cross-domain common feature extraction network and the small sample meta-learning framework, the problem of prediction model failure caused by data scarcity and frequent rule change of a new electricity market is solved, and the accuracy, generalization and adaptability of electricity price prediction in a data limited scene are remarkably improved.

The invention discloses a user side energy storage operation strategy generation method and system, and relates to the field of energy storage strategy generation, and the method comprises the steps: carrying out the time sequence coding of a historical electricity price to extract a periodic rule, converting the temperature, illumination and other parameters in a weather forecast, and holiday and festival event information into a structured vector, and carrying out the calculation of the structured vector; semantic alignment is achieved in the high-dimensional implicit space, and a feature combination containing external interference factors is formed. Then, an internal-external cooperative response coding mechanism is adopted, the electricity price time sequence characteristics and external factors are jointly coded to perform chain type interaction, and a finally generated electricity price prediction result comprehensively considers the dual effects of the external factors on load and market supply and demand, so that the energy storage system is supported to execute a precise charging and discharging strategy in an electricity price peak and valley interval; and the problem of strategy hysteresis caused by data splitting in the traditional method is effectively solved.

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 a settlement electricity price prediction method and device, electronic equipment and a storage medium, and the method and device are applied to the electronic equipment, and specifically comprise the steps: obtaining multiple groups of basic data; performing medium-and-long-term electricity price trend analysis and prediction processing based on the multiple groups of basic data to obtain a new energy medium-and-long-term electricity price; performing spot goods time-of-use electricity price prediction based on the multiple groups of basic data to obtain a new energy spot goods time-of-use electricity price; performing calculation based on the new energy spot goods time-of-use electricity price to obtain a new energy spot goods deviation settlement electricity price; and processing the new energy medium-and-long-term electricity price and the new energy spot goods deviation settlement electricity price according to the new energy kilowatt-hour electricity settlement price composition to obtain a new energy long-term settlement electricity price. According to the scheme, the new energy long-term settlement electricity price is obtained, so that investors can obtain a reliable investment basis for new energy investment development.

The invention discloses an electricity price prediction method and device, equipment and a storage medium, and relates to the technical field of electric power, and the method comprises the steps: obtaining a static variable and a time sequence variable corresponding to a target electricity utilization region; the static variable and the time sequence variable are input into a prediction model, a predicted power load corresponding to the target power utilization area is determined based on a model output result, and the prediction model is used for predicting the power load and comprises an input layer, a weight distribution layer, a dual coding layer and a decoding layer; and predicting the electricity price of the target electricity utilization area according to the predicted power load to obtain a predicted electricity price. According to the invention, the predicted power load of the target power utilization area is determined based on the model output result of the prediction model, and the electricity price of the target power utilization area is accurately predicted according to the predicted power load, so that a power supply party makes a scientific decision for the target power utilization area, and the power utilization party can reasonably use power in the target power utilization area. And the power supply cost of the power supply party and the power utilization cost of the power utilization party are reduced.