44 results about "Energy forecasting" patented technology

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 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 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.

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.

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.

This invention belongs to the field of energy simulation technology and relates to an energy flow network simulation system and method for steel enterprises. It includes: a system configuration module for configuring parameters based on user-inputted information and forming a structured configuration data package; a front-end operation module for configuring a visual interactive interface based on the configuration data package; generating a Gantt chart based on pre-set production and maintenance plans; and drawing a dynamic energy flow diagram based on user-defined equipment and energy nodes; and a back-end service module for calculating energy prediction results based on the Gantt chart, dynamic energy flow diagram, and configuration data package using a pre-set production and consumption model, and performing scheduling optimization when energy prediction results are unbalanced to obtain an energy scheduling balance result. This invention achieves collaborative simulation of production and energy, more intuitively displays the energy balance process, provides a more comprehensive simulation platform for the steel industry, and facilitates systematic management by enterprises.

The application discloses an energy data management method, system and device of a movable intelligent building cabin, and relates to the technical field of data management. The method comprises the following steps: deploying a sensor network on the movable intelligent building cabin, collecting energy data, environmental data and user behavior data in real time, and performing correlation fusion to generate an energy real-time state vector; constructing an energy output prediction model and an energy demand prediction model; performing energy prediction on the energy real-time state vector in a preset time window to obtain energy output prediction parameters and energy demand prediction parameters; performing energy regulation analysis on the energy output prediction parameters and the energy demand prediction parameters to determine target energy regulation strategy parameters, and performing dynamic management on energy data. The technical problems that the prior art is difficult to dynamically adapt to changes in the energy demand of the movable building cabin, resulting in energy waste and supply-demand mismatch are solved, the technical effect of intelligently regulating the energy management strategy and improving the energy supply-demand matching degree is achieved.

The present application relates to the technical field of energy security, and more particularly to a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method and system, comprising: obtaining active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output and load curve and numerical value; the post-disaster recovery model of active power distribution network is constructed in the guidance of elasticity, the adjustment strategy of distributed power generation resources is determined based on the topology reconstruction and island segmentation strategy method; the optimization scheduling model of repair personnel and network reconstruction coordination is constructed in the guidance of elasticity, and the second order cone relaxation method is used for relaxation processing to determine the fault repair strategy; through the active power distribution network topology parameters, the adjustable resource parameters and the renewable energy predicted output, the optimization problem is reconstructed based on the active power distribution network fault scene under the disaster; the optimization problem is solved with the minimum fault economic loss as the target, and the active power distribution network topology structure and the fault repair strategy are obtained.

The invention discloses a hydrogen production and energy supply simulation method and system, and relates to the technical field of hydrogen production, and the method comprises the steps: generating energy input information in an energy supply simulation system; calibrating the future energy prediction information according to the real-time energy output information to obtain calibrated energy prediction information; acquiring operation state information and hydrogen demand information of hydrogen production equipment in the energy supply simulation system; determining power distribution of the hydrogen production equipment; operating parameters of the hydrogen production equipment are adjusted according to the power distribution, the hydrogen production target and the hydrogen storage state; the operation state information of the energy supply simulation system is presented, and the operation process information of the energy supply simulation system is recorded. Optimized operation of the hydrogen production and energy supply system can be achieved, the efficiency, stability and reliability of the system are remarkably improved, and the actual operation risk is reduced.

The invention discloses a digital native model energy prediction method and system of a physically-driven computing power center. The method comprises the steps of obtaining time sequence mass flow, temperature data and geometric physical parameters required by operation of the cold storage tank, and setting prior information such as an initial to-be-identified parameter set and a flow direction to obtain input data and parameters; on the basis of energy conservation, constructing a layered ordinary differential equation model for describing the temperature change of each layer by using the input data and the parameters; using an adaptive step ODE solver to predict temperature distribution of each layer at each time point based on the hierarchical ordinary differential equation model according to input data and parameters so as to obtain predicted temperature; constructing a loss function by comparing the predicted temperature with an actual measurement value; a gradient is calculated based on the loss function, and parameters are adjusted by an optimization algorithm to minimize the loss function. By implementing the method provided by the invention, the model dimension can be reduced and the calculation can be simplified while the main physical mechanism is reserved.

The invention is suitable for the technical field of new energy and micro-grids, and provides an energy control method and device based on a heuristic proxy loss function, terminal equipment and a storage medium, and the method comprises the steps: collecting multi-source heterogeneous data corresponding to a building energy system with a photovoltaic and energy storage battery; inputting the multi-source heterogeneous data into the constructed prediction model to obtain an energy prediction result; wherein the energy prediction result comprises an electricity consumption prediction load and a photovoltaic prediction load; calculating an energy prediction error according to the energy prediction result and the energy actual result; constructing cost weighted heuristic loss and battery sensing heuristic loss based on the energy prediction error; training the prediction model based on the cost weighted heuristic loss and the battery perception heuristic loss to obtain a trained prediction model; and performing energy prediction control on the building energy system based on the trained prediction model. According to the method, accurate, efficient, economical and optimal control over the operation strategy of the building energy storage system is achieved.

The invention belongs to the technical field of energy prediction, and particularly relates to a long historical data-oriented periodic sensing wind power prediction method, which comprises the following steps of: obtaining wind power generation historical data; determining a data cycle value based on autocorrelation function analysis, and performing fragmentation processing and sequence decomposition on historical data according to a cycle length to obtain a seasonal component and a trend component; performing multi-scale feature enhancement on the fragmented data, and extracting rich time sequence dynamic information; and based on the enhanced fragment representation, outputting a plurality of fragment prediction results through a prediction model, and performing weighted fusion according to cosine similarity to obtain a final wind power generation power prediction result. And carrying out optimization training on the prediction model by adopting a time-frequency domain difference loss function. According to the method, periodic information and time sequence characteristics in the long historical window can be effectively utilized, the accuracy and calculation efficiency of wind power generation power prediction are remarkably improved, and reliable support is provided for power grid dispatching and energy management.

The invention relates to the technical field of energy prediction, and particularly discloses a total energy consumption prediction method and system based on an improved grey Markov model, and the method comprises the following steps: collecting the consumption data of a plurality of energy categories in a rated time period to obtain a plurality of historical data sequences, and carrying out the preprocessing and classification of the plurality of historical data sequences, obtaining a plurality of first-class data sequences and a plurality of second-class data sequences; respectively constructing a gray GM (1, 1) model and a gray Verhulst model based on the first type of data sequence and the second type of data sequence; introducing an improved Markov model, and correcting and calibrating prediction results of the grey model to obtain related corrected prediction values; and adding the two values to obtain a predicted value of the total energy consumption amount. According to the method, accurate prediction is performed according to the development characteristics of the energy categories, and the method has the characteristics of high prediction pertinence, high accuracy and high scientificity.

The invention relates to the technical field of energy prediction and control, in particular to a distributed photovoltaic output prediction and end side control method based on an LSTM and a time attention mechanism. After feature construction, standardization and dimensionality reduction, inputting a multi-step prediction model based on a long short-term memory (LSTM) network and a time attention mechanism to realize high-precision prediction of photovoltaic output; the model is quantized through an RKNN frame and then deployed on edge equipment, and end-side low-time-delay reasoning is achieved. In the operation process, online feedback correction and safety margin correction are executed in combination with actually measured power residual errors, and an online optimization strategy based on model predictive control is designed according to the control requirement of an inverter containing a power upper bound and a climbing rate constraint; and solving an optimal active power set value in real time by adopting a projection gradient method. According to the invention, the light abandoning rate and the power fluctuation are effectively reduced, and the grid-connected friend performance and the system operation stability are improved.

This application provides a method for predicting power energy supply and dispatch, belonging to the field of time series forecasting technology. The method includes: acquiring historical power generation parameters and dividing them into segments encoded as power time-slice sequences; performing anti-disturbance processing on the power time-slice sequences, and performing feature incrementing and bit-by-bit modulation to obtain bit-by-bit modulated features; capturing the time embedding vector of the power time-slice sequences through a time-modulated neural network, and generating an output response by combining basic linear terms and spline terms; generating grid nodes through an adaptive grid update mechanism, and generating a uniform grid node sequence through a uniform grid; linearly weighting and fusing the grid nodes and uniform grid node sequences to obtain a fused grid node sequence; performing boundary expansion and least-squares refitting of the spline weights on the fused grid node sequence to obtain an expanded node sequence, and updating the spline function weights to generate a prediction result representing future power generation. This improves the accuracy of power energy forecasting.

The application discloses a clean energy output prediction method and device based on meteorological mode identification and feature deviation correction, equipment and medium, including: constructing a daily scale meteorological feature vector according to a meteorological sample set; obtaining the membership of each meteorological sample under different meteorological modes, and determining the corresponding meteorological mode; screening the meteorological variables corresponding to each meteorological mode based on mutual information, and constructing the corresponding meteorological feature subspace, and obtaining the corresponding input vector based on the meteorological feature subspace and feature projection; constructing the corresponding clean energy output prediction model, and constructing a clean energy output prediction model set through a Bagging integration strategy; projecting the corrected to-be-predicted meteorological data to the meteorological feature subspace, and calling the corresponding clean energy output prediction model set to obtain a clean energy output prediction result. The application belongs to the field of energy prediction. The application can improve the accuracy of clean energy prediction.

The invention relates to an energy prediction method and device for multi-time scale trend modeling. The method comprises the steps that historical energy consumption data of target equipment and corresponding equipment fault records / inspection records are collected and processed to obtain a historical energy consumption data set; determining a prediction scale type, and if the prediction is long-term prediction, calculating a basic energy consumption index and constructing a multi-type trend fitting model; based on the multi-type trend fitting model and the basic energy consumption index, a long-time energy consumption prediction result is obtained through prediction; if the prediction is short-term prediction, performing short-term prediction to obtain a short-term energy consumption prediction result; and verifying the long-time energy consumption prediction result and the short-time energy consumption prediction result and outputting multi-scale energy consumption prediction. According to the method, multi-scale energy consumption accurate prediction is realized, the defects of insufficient precision and poor reproducibility of a traditional model are overcome, the range and length of multi-scale prediction are determined, the accuracy of time sequence trend characterization and the effectiveness of the prediction model are ensured, and a multi-type trend fitting model is constructed to realize accurate coupling of a time sequence trend and an energy consumption index.

The application relates to a satellite energy prediction method and device, computer equipment and a storage medium. State data and task plan data of a target satellite are acquired, a pre-trained hybrid prediction model is called based on the state data, a prediction SOC sequence of the target satellite in a prediction period is obtained, the remaining battery capacity in the prediction period is determined according to the prediction SOC sequence, and an energy budget value for load task planning is calculated according to the remaining battery capacity and the task plan data. Since the hybrid prediction model integrates ARIMA, CNN and LSTM, the advantages of different algorithms are fully utilized through structured division of labor and cooperation, so that the energy state and budget of the satellite in a complex and dynamic environment can be accurately predicted, reliable and forward-looking decision basis is provided for intelligent management of satellite energy, and long-term reliable operation and maximum service efficiency of the satellite can be ensured.

The application discloses a light storage direct flexible control method and system for a net zero carbon commercial complex, which comprises collecting real-time data including human flow, photovoltaic power generation, energy storage state, lighting and elevator operation, predicting future human flow after data cleaning and trend component extraction. Based on the human flow prediction value, peak period is determined, and the energy consumption weight of the system is evaluated combined with real-time data to complete energy demand prediction. With the goal of minimizing photovoltaic curtailment, the optimal photovoltaic output instruction is calculated to form a power generation scheme. According to the energy storage state, the discharge power, execution time and duration of the future period are optimized to develop an energy storage scheduling strategy. According to the energy prediction and lighting data, the light brightness level and switching strategy are optimized in different regions to form a lighting adjustment scheme; combined with the human flow and elevator data, the elevator operation frequency and scheduling strategy are optimized. All schemes are summarized to generate unified collaborative control instructions and execute them. The method realizes efficient use of energy.

This application applies to the fields of new energy and microgrid technology, and provides an energy control method, device, terminal equipment, and storage medium based on a heuristic proxy loss function. The method involves collecting multi-source heterogeneous data corresponding to building energy systems with photovoltaic and energy storage batteries; inputting the multi-source heterogeneous data into a constructed prediction model to obtain energy prediction results, including predicted electricity load and predicted photovoltaic load; calculating the energy prediction error based on the energy prediction results and actual energy results; constructing cost-weighted heuristic loss and battery-sensing heuristic loss based on the energy prediction error; training the prediction model based on the cost-weighted heuristic loss and battery-sensing heuristic loss to obtain a trained prediction model; and performing predictive control of the building energy system based on the trained prediction model. This method achieves accurate, efficient, and economically optimal control of the building energy storage system's operation strategy.

The invention relates to a multi-period multi-medium energy prediction closed-loop scheduling system and method, and the system comprises a data collection and processing module which is used for continuously collecting energy data of multi-energy equipment, and processing the energy data to obtain effective energy data; the back-end service module is used for generating a dynamic energy fluctuation prediction curve and constructing an optimal scheduling model according to the effective energy data of the historical scheduling period; the front-end operation module is used for generating an optimal scheduling instruction according to the optimal scheduling model and the effective energy data of the current scheduling period and issuing the optimal scheduling instruction to corresponding energy equipment for execution; the data acquisition and processing module is also used for feeding back equipment execution parameters and instruction execution conditions of the multi-energy equipment to the back-end service module; and the back-end service module is also used for generating a scheduling evaluation result of each energy device. According to the invention, the integrated closed-loop control of the dynamic energy scheduling instruction and equipment cooperative operation is realized, and the overall scheduling efficiency and operation economy of the multi-energy system are improved.

The invention relates to the technical field of power supply, in particular to a multi-energy cooperative power supply method and system and a storage medium. According to the invention, a self-adaptive power supply strategy which is deeply bound with the first-aid repair process is constructed by dynamically sensing the first-aid repair operation stage and intelligently dividing the load priority. When the mains supply is interrupted, the system can automatically reduce the secondary load and preferentially guarantee the power supply of the core load on the basis of accurate energy prediction under the condition of insufficient energy, so that the reliability and toughness of the emergency power supply system are remarkably improved. Meanwhile, according to the method, through periodic monitoring and adjustment, fine scheduling of energy is achieved, the power supply duration of the key load is effectively prolonged, and the efficiency and the success rate of first-aid repair operation are greatly improved.

The application discloses a digital native model energy prediction method and system of a physically driven computing power center. The method comprises: obtaining time series mass flow, temperature data and geometric physical parameters required for the operation of a cold storage tank, setting an initial to-be-identified parameter set and prior information such as flow direction, to obtain input data and parameters; based on energy conservation, a layered ordinary differential equation model describing the temperature change of each layer is constructed by using the input data and parameters; the temperature distribution of each layer at each time point is predicted based on the layered ordinary differential equation model by using an adaptive step ODE solver according to the input data and parameters, to obtain a predicted temperature; a loss function is constructed by comparing the predicted temperature with an actual measured value; the gradient is calculated based on the loss function, and the parameters are adjusted by an optimization algorithm to minimize the loss function. By implementing the method of the application, the model dimension can be reduced and the calculation can be simplified while the main physical mechanism is retained.

The invention provides a container cluster multi-target scheduling method and system cooperating with energy prediction, and relates to the technical field of computers, and the method comprises the steps: obtaining a power generation power prediction value; determining a container cluster scheduling strategy based on the size relationship between the reference power consumption of the container cluster and the generated power predicted value; obtaining a to-be-scheduled container group; calculating a long-term expected reward value corresponding to each candidate node allocated to the to-be-scheduled container group in the container cluster, and binding the to-be-scheduled container group with the candidate node with the highest long-term expected reward value to complete scheduling; wherein the container cluster scheduling strategy indicates the incidence relation between the long-term expected reward value and the power consumption generated by the container group to be scheduled in the container cluster.

This invention discloses a method and system for predicting energy consumption in low-carbon construction production lines. The invention utilizes ARIMA and artificial neural networks to design and develop a hybrid energy consumption prediction model for short-term energy forecasting in energy systems. First, electricity consumption data is classified using an ARIMA model. Then, the results obtained from the ARIMA model are used as one of the inputs to an artificial neural network model. The artificial neural network model simultaneously considers factors affecting electricity consumption, such as solar power generation, operating hours, and production volume. By integrating the correlations between these factors, a predicted value is output. Finally, the prediction values ​​from both the ARIMA model and the artificial neural network are combined to obtain the final energy consumption prediction result.