121 results about "Energy forecasting" patented technology

The invention discloses an integrated energy system model prediction control method based on deep reinforcement learning, which is used in the field of energy prediction control, and comprises the following steps: constructing a state prediction model according to the state, output increment and disturbance quantity of each unit in integrated energy equipment in a park; predicting the output state of each device of the system at each moment in the prediction time domain within the day, establishing a rolling optimization objective function based on a prediction value and a day-ahead plan value, and calculating an intra-day scheduling optimal control quantity; and constructing an intelligent agent, and learning weight parameters in the rolling optimization objective function by using a double-delay depth deterministic strategy gradient algorithm to realize optimization of model prediction control of the integrated energy system. According to the invention, through learning of the neural network on different scenes, the optimization efficiency and quality of the algorithm on the integrated energy system are improved.

The invention relates to the technical field of energy prediction, and discloses a hierarchical routing-based full-modal hybrid expert model energy prediction method, which comprises the following steps: S1, constructing a hierarchical expert network structure which comprises a plurality of expert layers, and each expert layer is configured to process feature data of a specific modal, wherein the expert layer is divided into a meteorological processing layer, an electricity price processing layer, a load processing layer and an illumination processing layer based on modal types; and S2, receiving multi-modal characteristic data, wherein the multi-modal characteristic data comprises meteorological data, electricity price data, load data and illumination data. A layered expert network structure and a dynamic routing distribution mechanism are constructed, a special expert layer is adopted to perform feature extraction and local prediction, and a dynamic distribution strategy of a routing module is combined, so that the compatibility of a prediction model to high-dimensional heterogeneous data is improved, the calculation complexity of model training and deployment is reduced, and the prediction efficiency is improved. And the real-time performance and resource constraint requirements in a virtual power plant scene are met.

The invention, which relates to the technical field of energy management, discloses a multi-target collaborative optimization prediction system and method for regional-level water-wind-light power generation power, and the system comprises a data collection module which is used for obtaining power generation power data, meteorological environment data and power grid load demand data in real time; the optimization processor is used for receiving the data fed back by the data acquisition module, and constructing a multi-target optimization model based on spatial-temporal feature extraction to generate a plurality of candidate optimization paths; the screening module is used for screening the candidate paths through a dynamic weight distribution algorithm and screening out an optimal collaborative optimization path; and the dynamic feedback module is used for carrying out online correction on the optimal collaborative optimization path in the real-time optimization process according to the power grid frequency deviation, the energy storage charge state and the meteorological abrupt change signal. The optimization processor constructs the multi-target optimization model based on the spatial-temporal characteristics, generates a plurality of candidate optimization paths and screens out the optimal path, and compared with a traditional single energy prediction and simple scheduling mode, the precision of power generation power prediction is improved.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

The invention discloses a city natural gas consumption prediction method based on an XGB-PSO-LSTM model, belongs to the technical field of energy prediction, and aims to solve the problem that a traditional statistical method such as time sequence analysis and regression analysis establishes a model based on historical data to predict future, and the prediction efficiency is high. The deep learning method has the advantages of processing the nonlinear relation of sequence data and long-term dependence, but the parameter adjustment is complicated and the adaptability is relatively weak.

The invention discloses an energy regulation and control method and system of a virtual power plant based on a prediction model, and relates to the field of virtual power plant operation control, and the method comprises the following steps: collecting energy data in the virtual power plant; integrating the wind power data and the load data, and performing feature extraction on the integrated data; based on the predicted power generation data of the distributed energy and the predicted demand data of the load side resources, constructing a preliminary energy dynamic balance prediction model; performing day-ahead load demand and distributed energy prediction by using the preliminary energy dynamic balance prediction model; according to the preliminary scheduling strategy, using a niche grey wolf optimization algorithm to adjust the operation state of each distributed energy and load side resource in real time; dynamically adjusting and optimizing the energy dynamic balance prediction model; and carrying out load demand and distributed energy prediction again by using the optimized energy dynamic balance prediction model. According to the method, the change of the power system can be responded in advance, and more efficient energy scheduling is realized.

The invention is applicable to the technical field of power transaction and energy management, and provides a power transaction aid decision management method, which comprises the following steps of: analyzing and predicting a power market environment through an energy large model according to power transaction environment information to obtain an energy prediction result, and performing decision management on the power market environment on the basis of the energy prediction result. The energy scheduling strategy is generated for the electricity market environment through the multi-target optimization model, and the electricity transaction in the electricity market environment is scheduled according to the energy scheduling strategy, so that the decision-making balance and comprehensiveness are improved, the method can better adapt to the complex scenes such as large-scale access of new energy and frequent change of electricity market rules, and the user experience is improved. And the market risk is reduced, so that the method has better adaptability and application prospects.

The invention discloses a photovoltaic power ultra-short-term prediction method, system and device and a storage medium, and relates to the technical field of new energy prediction. The method comprises the following steps: constructing an improved ensemble empirical mode decomposition algorithm to decompose original photovoltaic power data, and distinguishing and reconstructing a plurality of decomposed intrinsic mode functions to obtain reconstruction sequences of high, medium and low frequency components; for high-frequency and intermediate-frequency components, carrying out dynamic denoising by adopting an extended Kalman filtering algorithm; and inputting the original data, the low-frequency component, the denoised high-frequency component and the denoised intermediate-frequency component into a WTConv1d module, extracting multi-scale features through a wavelet filter and convolution operation, and inputting a multi-dimensional input matrix constructed by the multi-scale features and meteorological data into a Transform model for prediction. According to the method, noise interference can be effectively suppressed, multi-time scale feature expression is enhanced, and a self-attention mechanism is fully utilized to capture a cross-scale dependency relationship, so that the prediction precision and stability are improved.

The invention relates to the technical field of power system regulation and control, in particular to a park multi-resource intelligent flexible control method and device considering energy and carbon consumption collaboration, and the method comprises the steps: obtaining historical energy consumption data and historical carbon emission data in a target park; training the historical energy consumption data and the historical carbon emission data based on a feature-level constraint preference optimization algorithm to obtain a park multi-energy prediction model based on feature-level constraint preference optimization so as to predict energy demands in a future time period; constructing a park multi-resource optimization scheduling model according to the energy demand in the future time period; and based on a preset energy storage equipment charging and discharging strategy and a preset flexible load control strategy of the target park, generating a multi-resource energy-carbon cooperative flexible control strategy of the target park according to the park multi-resource optimization scheduling model. Therefore, the problem that an existing cooperative control method cannot meet the comprehensive requirements of the modern park in the aspects of energy conservation, carbon reduction, cost optimization and system stability is solved.

The invention belongs to the technical field of energy management, and particularly relates to a data acquisition energy management system and an energy prediction analysis method, and the system is characterized in that a data acquisition gateway acquires data measured by a sensor or acquires real-time operation data of equipment, and transmits the data to a data layer according to a set protocol; the data layer receives the data transmitted by the data acquisition gateway, normalizes and standardizes the transmitted data through a data processing engine in the data layer, and stores the data in a database; meanwhile, the data layer trains an energy prediction model, wherein the energy prediction model is used for converting the obtained data into data for predicting the energy use trend, anomaly detection and energy-saving suggestion; the application layer monitors energy consumption in real time and performs load balance and energy optimization control by controlling operation of energy equipment; the display layer provides a user interface and a visual report to display energy prediction data and an analysis result. And the data processing efficiency and the energy prediction precision are improved.

The invention relates to the cross technical field of weather forecast and energy prediction, and discloses a physical-data fused weather-wind and light power combined prediction method and system, and the method comprises the steps: constructing an initial combined prediction model, and enabling a weather forecast model and a wind and light power prediction model to be connected into a whole through the model; performing end-to-end training on the initial joint prediction model by adopting a historical meteorological data sample and a historical wind-solar power generation power data sample; and inputting the meteorological data of the target area into the trained joint prediction model, and outputting a wind-solar power generation power prediction value in a future time period. By adopting the end-to-end joint training method based on the differentiable calculation path, global collaborative optimization of the weather forecast model and the wind and light power prediction model is realized, so that the core problem of power prediction misalignment caused by unidirectional transmission and accumulative amplification of the weather model error in the traditional series architecture is solved; and the precision of final power prediction is ensured from the system level.

The invention discloses a distributed photovoltaic user power behavior control scheduling method and system, and the method comprises the steps: deploying an edge controller at a photovoltaic user local device, and collecting power data in real time; a sliding window autoregression model is adopted to carry out short-term load prediction, and an edge controller realizes energy storage state adjustment and load matching balance by constructing an edge node objective function; carrying out constrained quadratic programming solution on the edge node target function, taking a solution result as a node local scheduling strategy, and regularly uploading a current state and a control result to the cloud; the cloud optimization layer collects all edge node state data, constructs a global system state set, and establishes a multi-target joint scheduling model on the cloud optimization layer; and solving and optimizing the multi-target joint scheduling model to generate a global scheduling strategy, and issuing the global scheduling strategy to each edge node. According to the invention, energy prediction and real-time load scheduling are independently executed on the edge side controller on the basis of local data, and the response speed is improved.

The invention discloses a multi-energy complementary distributed comprehensive energy system and a method thereof, and relates to the technical field of energy management. Comprising a renewable energy power generation module, a fossil energy power generation module, an energy storage module, an energy conversion module, a user terminal and an intelligent control center. The renewable energy power generation module comprises a solar cell panel array and a wind generating set, and the renewable energy power generation module is intelligently regulated and controlled through the energy prediction and intelligent matching control system in combination with big data analysis and a machine learning algorithm. Through cooperation with an electric energy collection unit, a power distribution unit and an intelligent protection unit of the hybrid energy coupling module, maximum utilization of renewable energy is realized, overall power generation efficiency and energy utilization efficiency are improved, stability and reliability of the system are enhanced, diversified energy requirements of users are met, and meanwhile, the system has fault diagnosis and early warning functions. Safe and stable operation of the system is guaranteed.

Techniques are described for generating energy interval notifications. An example can include a computing system configured to access generation source information, grid conditions information, and price information. The computing system can generate forecasted values based at least in part on historical information, where the forecasted values are generated for a first time interval. The computing system can also determine an energy forecast for the first time interval, where the energy forecast is based at least in part on the forecasted rate and the forecasted values.

The invention discloses an intelligent building integrated control method and system based on cloud computing, relates to the technical field of building integrated control, and adopts an environment sensing module to collect building environment data in real time, ensure the accuracy and timeliness of the data and provide basic data support for intelligent regulation and control. And through the energy prediction and scheduling module, the future power load Ppr and the personnel density Dus are dynamically predicted based on the time sequence prediction model and the regression model, the energy supply is optimized, and the unnecessary energy consumption is reduced. And a user behavior analysis module is adopted to accurately identify the peak period, the equipment use frequency and the energy consumption mode in the building based on the personnel density and the equipment use data, so that the energy waste is reduced. The operation mode of the air conditioner heating and ventilating system is dynamically adjusted, continuous operation of high-power refrigerating or heating equipment in the low-load period is avoided, and therefore unnecessary energy consumption is reduced. Intelligent light adjustment is realized, illumination brightness is adjusted according to personnel activity conditions, and long-term invalid operation of the illumination system is avoided.

The invention belongs to the technical field of energy prediction, and particularly relates to an enterprise energy consumption prediction method, system and equipment, and the method employs a first energy consumption prediction model to predict linear data of a historical data set, obtains a first model prediction result, and carries out the subtraction based on the historical data set and the first energy consumption prediction result, thereby obtaining an energy consumption prediction result. And the first energy consumption prediction model is used for obtaining energy consumption data set residual data, the second energy consumption prediction model is used for predicting the energy consumption data set residual data to obtain a second model prediction result, and the first model prediction result and the second model prediction result are combined to obtain a final energy consumption prediction result. According to the method, the combined model composed of the ARIMA and the BiLSTM is adopted for prediction, the model prediction precision can be effectively improved, parameter optimization is conducted on the BiLSTM model through the chaos chicken flock optimization algorithm, the optimal parameters are substituted into the BiLSTM model for training, the training time can be shortened, and the search speed can be improved.

The invention relates to the technical field of energy prediction, in particular to a cloud new energy state prediction scheduling method and system based on a deep network. The method comprises the following steps: acquiring cloud new energy network data; performing network topology analysis on the cloud new energy network data to generate cloud new energy network topology data; performing cloud new energy monitoring data acquisition on the cloud new energy network data according to the cloud new energy network topology data to generate standard cloud new energy monitoring data; and performing energy scheduling management stage classification on the standard cloud new energy monitoring data to generate energy scheduling management stage data. Through standardized data integration, full-life-cycle management, real-time dynamic prediction and energy coordination optimization, the problems of insufficient dispersibility, real-time performance and efficiency of cloud new energy state prediction scheduling in the prior art are solved.

The invention discloses a virtual power plant auxiliary analysis system and method, a storage medium and an electronic device, and relates to the field of computers, and the virtual power plant auxiliary analysis system comprises a data integration module which is used for obtaining the operation data of a plurality of energy data sources, and obtaining a plurality of first operation data of a first time period; the prediction scheduling module is connected with the data integration module, and is used for carrying out energy supply and demand analysis according to the various first operation data, and generating a first scheduling strategy according to an obtained energy supply and demand analysis result; the energy operation and maintenance management module is connected with the prediction scheduling module and used for performing energy scheduling on the multiple energy data sources according to the first scheduling strategy in a second time period, and the second time period is later than the first time period; through adoption of the scheme, the problem that the existing energy prediction and scheduling algorithm lacks deep understanding and accurate prediction of a complex energy system in the prior art is solved.

The present invention relates to the field of power grid technology, and specifically to a method and system for coordinated planning of power grid sources, grids, loads and storages taking into account multiple types of energy storage, including: obtaining future load data of the power grid using a preset load forecasting model based on historical load data of the power grid; obtaining future energy supply data using a preset energy forecasting algorithm; obtaining future multiple types of energy storage data using a preset multiple types of energy storage forecasting algorithm; and using the future load data of the power grid, the future energy supply data and the future multiple types of energy storage data to coordinate planning based on the goal of maximizing load satisfaction. The present invention can better cope with load fluctuations and uncertainties in energy supply by making multi-faceted predictions on the energy side, the load side and the energy storage side, and making coordinated planning based on the prediction results. It ensures that the electricity demand of users can be met in all situations, reduces power outages, and promotes the development of clean energy.

The invention discloses an energy prediction management method and system based on a time sequence large model, and relates to the field of energy prediction, and the method comprises the steps: obtaining enterprise energy consumption historical data, environment data and enterprise operation data, and constructing the enterprise energy consumption historical data into a time sequence with the length T according to a time sequence; dividing the time sequence into a plurality of subsequences according to a preset length L, executing attention calculation on the plurality of subsequences through a time sequence large model to obtain association strength among the sequences, and retrieving a plurality of historical fragments most similar to a current energy consumption mode from enterprise energy consumption historical data according to the time sequence large model based on the association strength; generating an energy consumption prediction value of a preset future time period according to the historical fragment, the environment data and the enterprise operation data based on a time sequence large model; and sending the energy consumption prediction value to the target client. By implementing the method, the accuracy of enterprise energy prediction can be improved.

The application discloses a kind of cascade reservoir group scheduling method and system for clean energy consumption, first, obtain the long sequence of the relative error of clean energy each period output and its mean, variance, according to which the output scene set is generated by using clean energy prediction output;Second, a large-scale scene set is reduced to several typical output scenes using clustering algorithm, and then the photovoltaic output process and its occurrence probability of different scenes are obtained;Finally, construct the applicable cascade reservoir group joint scheduling model, and then solve to obtain reasonable and feasible scheduling scheme using efficient optimization algorithm.The method disclosed by the application can quickly respond to the randomness and uncertainty characteristics of the clean energy output process, effectively handle the coupling operation constraints of cascade power stations, significantly improve the model calculation efficiency and scheduling scheme quality, and is suitable for the cascade reservoir group joint scheduling problem for clean energy consumption.

The invention discloses a regional energy demand prediction processing method and electronic equipment. The method comprises the following steps: acquiring historical industrial data corresponding to a target area in a plurality of industrial dimensions; determining multiple groups of influence factor indexes influencing the energy demand; multiple energy prediction scenes are determined, and the multiple energy prediction scenes correspond to different carbon emission strategies; based on the historical industrial data corresponding to the plurality of industrial dimensions and the plurality of groups of influence factor indexes, adopting a long-term energy substitution planning system LEAP model to obtain energy demand prediction results corresponding to the target area in the plurality of energy prediction scenes, the source demand prediction result comprises an electric energy demand prediction result, a natural gas demand prediction result and a cold / heat load demand prediction result. According to the invention, the technical problems of lack of pertinence and low accuracy of energy demand prediction caused by incomplete consideration factors of regional energy demand prediction in related technologies are solved.

This invention discloses a method for predicting energy data in semiconductor factories based on the fusion of multiple time-series models. The method includes: cleaning and defining the features of energy data; selecting and training multiple individual models based on the data characteristics of the processed energy data; selecting a suitable individual model based on scenario discrimination indicators, wherein the scenario discrimination indicators include fluctuation amplitude and seasonal cycle; the fluctuation amplitude is calculated by the standard deviation of the data or the range within a moving window, and the seasonal cycle is obtained by detecting the cycle strength using an autocorrelation function; fusing the prediction results of the selected models; evaluating the fused model and making energy predictions based on the evaluation results. This invention can adaptively select the optimal model, ensuring the accuracy and stability of energy prediction.

The invention discloses an energy-based economic key element linkage prediction method and system, and relates to the technical field of economics and prediction modeling, and the method comprises the steps: determining and collecting a data source, and carrying out the preprocessing of the obtained data; exporting key business data based on the multi-dimensional data model, and constructing an energy consumption data and economic growth prediction model; training an energy consumption data and economic growth prediction model, and performing result evaluation and model optimization; and economic growth and energy prediction are carried out by using the optimized model, and analysis is carried out according to a prediction result and a corresponding decision is provided. By constructing the data cube model, data from different fields can be efficiently integrated, multi-dimensional comprehensive analysis is realized, and the comprehensiveness of the prediction model is improved; according to the method, the optimized model is utilized, a more flexible nonlinear modeling method is adopted, the complex relation between economic growth and energy consumption is better captured, and the prediction accuracy is improved.

The invention discloses an energy system scheduling method and device and a medium, and the method comprises the steps: inputting an energy characteristic parameter corresponding to an energy system into a preset energy prediction model, and outputting a corresponding energy demand prediction result; wherein the energy demand prediction result refers to a confidence interval formed by energy demand prediction values with different confidence degrees under each prediction time step; according to the boundary value of the confidence interval, constructing a power balance constraint relationship of the energy system, and based on the balance constraint relationship, solving a pre-constructed multi-objective optimization function to obtain a plurality of scheduling strategies; and selecting a target scheduling strategy from the plurality of scheduling strategies, and scheduling the energy system through the target scheduling strategy.

According to the virtual power plant-oriented energy storage power station combined regulation and control method and system, historical data of an energy storage power station, a photovoltaic power station and a wind power station are collected, a data set is constructed, and an energy prediction model is established; predicting energy prediction data in a future time period by using the trained energy prediction model; obtaining real-time price and supply and demand data of the electricity market; performing time period division on the data through an extreme random tree model ERT, and distributing real-time electricity price interval labels; combining the data subjected to time period division and label distribution into a scheduling data set; according to the scheduling data set, defining an optimization target, and constructing a virtual power plant multi-energy collaborative scheduling model; energy scheduling constraint conditions are constructed, a scheduling scheme is obtained, control instructions are sent to an energy storage power station, a photovoltaic power station and a wind power station, and real-time control over all energy main bodies is achieved. The power market fluctuation can be responded in real time, the multi-energy collaborative scheduling of the virtual power plant is optimized, the energy configuration efficiency is improved, and the operation cost is reduced.

This invention discloses an energy regulation method and system for virtual power plants based on a predictive model, relating to the field of virtual power plant operation control. The method includes the following steps: collecting energy data from the virtual power plant; integrating wind power data and load data, and extracting features from the integrated data; constructing a preliminary energy dynamic balance prediction model based on predicted generation data of distributed energy sources and predicted demand data of load-side resources; using the preliminary energy dynamic balance prediction model to predict day-ahead load demand and distributed energy; adjusting the operating status of each distributed energy source and load-side resource in real time using the niche gray wolf optimization algorithm according to the preliminary dispatch strategy; dynamically adjusting and optimizing the energy dynamic balance prediction model; and re-predicting load demand and distributed energy using the optimized energy dynamic balance prediction model. This invention can respond to changes in the power system in advance, achieving more efficient energy dispatch.

The invention discloses a factory integrated energy system optimization operation method considering a production network and an energy network. The method comprises the following steps: establishing a carbon emission knowledge graph of each link of the production network and a carbon emission knowledge graph of each device of the energy network through an energy-saving and carbon-reducing intelligent agent fused with a large language model, generating an energy-saving and carbon-reducing diagnosis report, and formulating an energy-saving and carbon-reducing reconstruction scheme; in a current system operation stage, carrying out analogue simulation on the optimal energy-saving and carbon-reducing reconstruction scheme, and respectively establishing energy prediction models of a production network and an energy network; the method comprises the following steps of: sensing energy consumption predicted values, carbon emission intensity predicted values and carbon emission predicted values of a production network and an energy network in each time period in the future through a decision-making agent, and judging whether a high-carbon low-efficiency link / equipment exists or not through an energy demand and a production scheduling plan of the production network in each time period in the future and an energy supply plan and a global carbon emission index of the energy network in each time period in the future; if yes, a system strategy adjustment mechanism is triggered, and a low-carbon economic optimization operation model of the dynamic alliance of the production network and the energy network is established.

This invention relates to the field of energy forecasting technology, and in particular to a method and system for short-term energy load forecasting based on an improved Transformer using SE-Block. The method includes reversible normalization preprocessing of acquired multivariate load sequence data; feature extraction and fusion of the preprocessed data using improved cross-scale interactive patching, including multi-scale feature extraction, cross-scale interactive alignment, residual correction, and dynamic fusion; and feature filtering of the fused features based on a channel attention mechanism, including feature response based on improved SE-Block and nonlinear interaction of context vectors. This invention addresses the non-stationarity of actual load caused by meteorological conditions and user behavior. By automatically eliminating noise interference among multiple variables, the model accurately depicts the fluctuation details of the load curve, demonstrating its robustness in multivariate load forecasting for integrated energy systems.