1046 results about "Weather prediction" patented technology

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses an AI-NWP three-dimensional closed-loop bidirectional dynamic feedback coupling method for extreme rainfall event area simulation and a program product, and belongs to the technical field of meteorological numerical simulation and artificial intelligence fusion. According to the method, a mid-term forecast field is generated based on an AI global weather prediction model, an analysis field is constructed by assimilating multi-source observation data, and a high-resolution NWP region mode is driven to perform rolling simulation. Furthermore, a space-time residual field is constructed by using the difference between an NWP region simulation result and live data, a residual learning model is trained, an AI model prediction structure is fed back and corrected, and dynamic weight adjustment updating of the AI prediction model is realized. A closed-loop two-way feedback system among AI output, NWP simulation and residual evaluation is integrally formed, the space structure reduction capability and the area positioning precision of a medium-term heavy rainfall event are effectively improved, the continuous predictability and the simulation credibility of an extreme weather process are remarkably enhanced, and the method has good stability, universality and engineering expansion value.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention discloses a medium and long term weather prediction system based on a physical law correction deep learning technology, and the system comprises a high-resolution data input module, a multi-resolution network construction module, a sub-graph processing module based on a graph neural network, and a linear attention processing module. The system comprises an advanced attention processing module used for reducing secondary calculation complexity to linear complexity, a feature fusion and adaptive fusion module, a multi-level decoding and physical corresponding area optimization correction model, a rolling forecasting module and a multi-step training fine tuning module based on DORA. The medium and long term weather prediction system based on the physical law correction deep learning technology can process multi-resolution input data, and realizes high-precision and long-time weather prediction. Furthermore, global weather forecast with the resolution ratio of 16 days and 13 kilometers can be achieved, and meanwhile refined forecast with the higher resolution ratio is achieved in China.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention discloses a photovoltaic module cleanliness evaluation method and system based on multi-dimensional data fusion. The method comprises the following steps: acquiring multi-dimensional data of a photovoltaic power station, including generating capacity data, power station state data, irradiation data, meteorological data, geographic position data, cleaning record data, weather forecast data and economic index data; preprocessing the data; constructing a multi-dimensional feature; a pre-trained Transform architecture time sequence model is adopted to carry out training; evaluating the cleanliness state of the photovoltaic module in real time and predicting the change trend; and determining the optimal cleaning time and generating decision suggestions based on the cleanliness evaluation result and economic analysis. Through multi-dimensional data fusion and a large model algorithm, precise evaluation and scientific decision making of the cleanliness of the photovoltaic module are realized, the evaluation accuracy is improved by 15-20%, and the method is suitable for photovoltaic power stations at different geographic positions and under different climate conditions.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention relates to the field of optical storage, and particularly discloses an optical storage system cooperative control method and device, a terminal and a medium, and the method comprises the steps: collecting multi-dimensional data in real time, including photovoltaic array data, user side load data, power grid side information data, meteorological data and weather forecast information; generating a photovoltaic power generation power prediction curve, a user load demand prediction curve and a power grid electricity price prediction curve by using the multi-dimensional data through a machine learning model; and taking the current state of the optical storage system and each prediction curve as input parameters, carrying out optimization problem solving based on a multi-objective optimization function and constraint conditions through a model prediction control algorithm, generating an optimal control sequence in a period of time in the future, and controlling corresponding equipment through the optimal control sequence. According to the method, the response speed to uncertainties such as illumination abrupt change and load fluctuation is increased, source-storage-load-network coordination is realized, the sub-optimal problem of independent control of each unit is avoided, the operation cost is reduced, and renewable energy consumption is improved.

The invention relates to the technical field of meteorological prediction, in particular to a meteorological prediction precision improvement method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a plurality of initial weather prediction models based on a Transform structure in combination with a PIDL (Precision Independent Design Language) algorithm; taking the power curve of the wind turbine generator and the performance characteristic curve of the photovoltaic module as constraint conditions, and integrating the constraint conditions into each initial meteorological prediction model to obtain a plurality of basic meteorological prediction models; based on a preset automatic hyper-parameter tuning tool, performing hyper-parameter optimization on each basic meteorological prediction model in combination with a cross validation algorithm; fusing the plurality of basic meteorological prediction models after hyper-parameter optimization by adopting an integrated learning algorithm to obtain a target meteorological prediction model; and according to the meteorological characteristic data and the target meteorological prediction model, the target meteorological prediction result is determined, and the meteorological prediction precision is improved.

The invention belongs to the technical field of wind power prediction, and particularly relates to a wind power prediction method and system based on multi-source data fusion. According to the method, a cascade prediction architecture composed of a wind speed prediction model and a power prediction model is constructed, and the wind speed prediction module extracts cross-variable association features of multi-dimensional meteorological parameters from numerical weather forecast data by adopting a variable-level attention mechanism and a patch-level attention mechanism; capturing a long and short time dependence mode from the actually measured historical operation data; the power prediction model decomposes a predicted wind speed sequence through one-dimensional average pooling, and constructs a nonlinear power mapping model. Besides, an attention fusion module is provided, a learnable global variable is introduced as an information exchange bridge, information interaction between meteorological data and actually measured data is realized, and the collaborative utilization efficiency of multi-source data is effectively improved. Experiments show that the method is superior to an existing prediction model based on deep learning.

The invention relates to the field of photovoltaic power prediction, in particular to a photovoltaic power prediction method, and the method comprises the steps: obtaining the spectral response function data of different photovoltaic modules in a photovoltaic power station, and measuring the spectral irradiance distribution of the surface of each photovoltaic module in real time; according to the spectral response function data and the spectral irradiance distribution, calculating the effective spectral irradiance of each component type in a grouping manner according to the component types; counting the temperature coefficient of the batch to which each photovoltaic module belongs according to historical data, and establishing a module-level temperature-power correction factor; according to the string topological relation, string-level temperature correction power is obtained, and the string-level temperature correction power, the effective spectral irradiance and the global irradiance data of the numerical weather forecast are output into a corrected power station-level photovoltaic power predicted value; the problem of low photovoltaic power prediction precision caused by inaccurate modeling due to parameter difference and temperature influence of photovoltaic modules in photovoltaic power station power prediction is solved.

The invention discloses a photovoltaic power generation power prediction method and system based on multi-source data fusion. The method comprises the steps of obtaining historical operation data of a target photovoltaic power station and historical meteorological data corresponding to the historical operation data; based on the historical operation data and the historical meteorological data, constructing and training a historical deviation mode correction downscaling model, and correcting future coarse resolution meteorological forecast data provided by a numerical weather forecast model to obtain refined meteorological forecast data of a target photovoltaic power station site scale; on the basis of the refined meteorological prediction data, utilizing a physical-statistical coupling prediction model to predict the future generation power of the target photovoltaic power station; according to the historical deviation mode correction downscaling model, a targeted correction function is established by analyzing a systematic deviation mode between a theoretical prediction value and an actual observation value in historical data, and conversion from coarse resolution prediction to site scale microscopic meteorology is realized, so that the photovoltaic power generation power prediction precision is improved.

The invention discloses a sand and dust vertical flux high-precision inversion method based on a laser radar. The method comprises the following steps: acquiring multi-wavelength back scattering and polarization information by using a 355 nm, 532 nm and 1064 nm three-wavelength polarization laser radar system; constructing a five-dimensional optical feature vector space, and combining a support vector machine classifier to realize automatic identification of dust particles; a variational data assimilation technology is adopted to fuse radar observation and numerical forecasting information to invert a three-dimensional wind field; inverting sand and dust mass concentration vertical distribution based on the corrected particle spectrum distribution model; flux calculation and uncertainty quantization are realized through adaptive weighted fusion and a Monte Carlo method; the method has the characteristics of high temporal-spatial resolution, high precision, strong adaptability and the like, and can be widely applied to the fields of weather forecast, environment monitoring, climate research and the like.

The invention provides a consumption reduction method and device for a wind and light energy storage complementary thermal power plant system, and the method comprises the steps: obtaining the wind speed, illumination intensity, energy storage charge state, auxiliary power load curve, power grid dynamic electricity price signal and thermal power generating unit auxiliary machine operation parameters of the thermal power plant system, and taking the parameters as a data set; based on a weather prediction model, a load prediction model and an electricity price fluctuation model, generating a wind power generation output prediction value, a photovoltaic power generation output prediction value, a load demand prediction value and an electricity price interval prediction value in a future preset time period according to the data set, and taking the prediction values as prediction results; by taking minimization of plant power cost and maximization of renewable energy consumption as targets, generating an energy storage charging and discharging instruction, a wind power generation and photovoltaic power generation grid-connected priority sequence and a thermal power auxiliary engine regulation and control strategy according to the data set and the prediction result to serve as control instructions; control instructions are executed through control equipment in the wind-solar energy storage complementary thermal power plant system, and energy conservation and consumption reduction are achieved by integrating wind energy, solar energy and an energy storage system.

The invention discloses a real-time calculation method and system for distributed photovoltaic power generation meteorological elements, and solves the technical problem of regional distributed photovoltaic power generation meteorological information loss. The method comprises the following steps: selecting adjacent sites based on power station operation data, and repairing target site data by using adjacent site data; constructing an irradiance calculation model by using the reconstructed power data, and establishing a model among numerical weather forecast, distributed photovoltaic station power, irradiance and environment temperature; and based on the established model, calculating the solar irradiance and temperature of the position of the distributed photovoltaic station by taking the real-time power and the current value of the numerical weather forecast as input. In order to complement the power of the distributed photovoltaic station, a meteorological information calculation model is established, a high-cost-performance and high-precision solution is provided for complementing the meteorological information of distributed photovoltaic power generation, and the accuracy of output power prediction of the distributed photovoltaic station is improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention discloses a fog boundary layer parameterization scheme correction method based on a multi-scale physical coupling network, relates to the crossing field of numerical weather forecast and artificial intelligence, and aims to improve the performance of different boundary layer parameterization schemes in a WRF mode, construct a fusion framework of a physical mode and deep learning, adopt a double-branch heterogeneous network structure, and improve the performance of a fog boundary layer parameterization scheme. Establishing a nonlinear mapping relation between different parameterization scheme deviations and large eddy simulation turbulence characteristics by combining a space-time attention mechanism; by correcting systematic deviation existing in a parameterization scheme, the analysis capability of a turbulence structure is improved, and physical description of a boundary layer process is optimized; compared with a traditional boundary layer scheme, the method has the advantages that the fog zone simulation precision is remarkably improved, the fog zone visibility, the liquid water content and the root-mean-square error of a liquid water path are reduced by 66.7%, 50% and 58.3% respectively, the problem that turbulence intermittent characterization is insufficient under a stable boundary layer in a traditional method is effectively solved, and a new normal form is provided for refined forecasting of the fog generation and elimination process.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the field of numerical weather forecast and deep learning, and discloses a numerical forecast wind field data error correction method and system based on a neural network, a medium and equipment. A data set obtained after data preprocessing is divided into a training set, a verification set and a test set, and space-time matching and normalization are conducted on the data set; establishing a many-to-many variable mapping relationship in a horizontal space by using the training set, expanding a time dimension on the basis of the established multiple mapping relationship, and establishing a 3D U-Net-based deep learning model; performing training and parameter tuning on a 3D U-Net-based deep learning model by using the training set and the verification set, and constructing a deep learning correction model of multi-element multi-time forecast; and correcting the test set by using the trained deep learning correction model. According to the method, the problem of multi-forecast aging and multivariable collaborative correction can be solved.

The invention relates to the technical field of weather forecast, in particular to a weather downscaling method fusing numerical weather forecast and AI weather forecast, which comprises the following steps: acquiring numerical weather forecast data and satellite remote sensing observation data, and performing unified preprocessing, interpolation and mapping to generate refined forecast data and observation reference data. Then, local compensation based on a deep neural network and model-independent element learning are adopted to carry out regional adaptive compensation, a joint objective function is constructed to implement variational assimilation and joint optimization, and meanwhile, a generative adversarial network and a graph neural network are combined with reinforcement learning to realize error compensation and node-level adaptive correction; and finally, continuously updating parameters through closed-loop feedback iteration to generate updated forecast data. According to the method, the problems of insufficient local details and poor model adaptability in the downscaling process in the prior art are effectively solved, and the forecasting precision and robustness are improved.

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.

The invention relates to the technical field of photovoltaic prediction, in particular to a distributed photovoltaic power prediction method and system based on high-dimensional gridding numerical weather forecast, and the method comprises the steps: carrying out the standardization of the numerical weather forecast data and photovoltaic power historical data of a target region, and achieving the time-space alignment based on a preset grid, generating a gridding data set; utilizing convolution processing to extract local space features, and converting and fusing the local space features into a feature sequence containing space and historical time sequence information at the same time; modeling is carried out through an encoder-decoder architecture, an encoder excavates historical power dependence, and a decoder dynamically couples future meteorological characteristics with historical power through an attention mechanism and outputs a grid-level predicted value; aggregating to obtain a system total power prediction result; by establishing a unified space-time grid, refined alignment of data is realized, cross-space-time dynamic fusion is performed in combination with convolution and an attention mechanism, and prediction precision and stability can be kept in complex weather.