14 results about "Climate model" patented technology

The application belongs to the field of sub-seasonal climate prediction, and provides a sub-seasonal climate prediction method and system based on the combination of dynamic model downscaling and machine learning downscaling, which comprises the following steps: S1, generating initial field and model boundary field information required for dynamic downscaling based on global climate model output data; S2, driving regional climate model to perform dynamic downscaling by using the initial field and model boundary field information; S3, generating input field required for machine learning downscaling based on the output circulation field information of the regional climate model; S4, performing machine learning downscaling correction optimization on the output circulation field of dynamic downscaling based on a convolution model; S5, performing machine learning super-resolution based on the output data of machine learning downscaling correction; and S6, generating sub-seasonal prediction information of double downscaling of dynamic model and machine learning. The application utilizes the complementary advantages of dynamic downscaling and deep learning downscaling, improves the sub-seasonal prediction skill, and copes with new challenges brought by climate change.

The application discloses a satellite remote sensing data collection and preprocessing method and system, and relates to the technical field of satellite navigation. The method specifically comprises the following steps: collecting remote sensing images of a semiarid region through a satellite, analyzing pixel radiation and brightness gradient to identify bare land water features, constructing a leading atlas, extracting a boundary and registering to generate a standard structure, combining a climate model to correct to generate a reflectivity layer, transferring instructions when parameters are insufficient, reconstructing a queue by using a shortest algorithm to complete radiation calibration, atmospheric correction and geometric registration, and forming a preprocessing layer set. In the application, multi-band division is combined with radiation gradient calculation to enhance the separation degree of ground objects, boundary feature extraction and geometric registration are used to eliminate imaging distortion, a dynamic radiation model is fused with climate parameters to correct reflectivity, a double-threshold value is monitored to optimize a transmission strategy and avoid interruption, a priority algorithm is used to reconstruct a queue to reduce data delay, multi-level quality control is used to cooperatively process to form standard data, and the temporal and spatial reliability and application value are improved.

This invention belongs to the field of medium- and long-term power generation in new energy, and discloses a medium- and long-term power forecasting method and related equipment based on ERA5 reanalysis data. This method uses multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, obtains the climatological average irradiance, and constructs an error correction model by combining it with historical measured data from the power plant. This allows for medium- and long-term power forecasting using historical data from the same period of the power plant's climatological output. By using multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, this method can accurately capture the climatological average irradiance of the area, effectively solving the forecasting problem caused by the lack of historical data for newly built or short-term operating photovoltaic power plants. Using this method is of crucial practical significance for power plants to reasonably declare medium- and long-term trading volumes in electricity transactions and reduce revenue losses caused by positive and negative volume deviations, significantly enhancing the competitiveness of power plants in the electricity market.

This invention provides a method for climate-adaptive reconfiguration of power distribution networks, comprising: obtaining typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models; constructing power distribution network optimization models based on the typical, extreme, and expected climate datasets respectively; performing a risk quantification assessment of the power distribution network based on the optimization models to form candidate reconfiguration schemes; and selecting the candidate reconfiguration scheme with the smallest normalized integration assessment value as the climate-adaptive reconfiguration scheme through normalized integration evaluation of the expected net present value and expected integration level of the power distribution network under each candidate reconfiguration scheme. This invention enables power distribution network reconfiguration to adapt to various climate scenarios.

The present application belongs to the technical field of new energy detection, and relates to a method, system and device for testing albedo of a photovoltaic power station and a storage medium. The method comprises: obtaining a total plan layout of a target photovoltaic power station and high-altitude image data; dividing a photovoltaic power station region by using a classification algorithm based on the total plan layout and the high-altitude image data to obtain a plurality of surface feature regions; determining a plurality of typical points of each surface feature region to obtain a single-point albedo of each typical point; determining the areas of the plurality of surface feature regions by using the high-altitude image data; and calculating a total albedo of the target photovoltaic power station by using a weighted average algorithm based on the single-point albedo of the typical points and the areas of the surface feature regions. The present application significantly improves the accurate evaluation of albedo under complex surface environment through multi-dimensional quantitative analysis of albedo, and provides key data support for ecological effect evaluation and climate model construction of photovoltaic power stations.

A plurality of climate models for a variety of hazards and / or initial conditions to produce a collection of Hazard Exposure (Risk) Maps. The Hazard Exposure Maps are transformed to high resolution (and may be filtered) and convolved with auxiliary data related to one or more hazards. The now super resolution maps are input to a learning engine along with spatiotemporally harmonized historic events and active near real-time events to produce a calibrated model (Asset Level Exposure Risk Estimation Model) that utilizes the super resolution maps and asset location(s) to project risk for each asset. The projected risk may be provided to asset operators / owners, initiate signal alerts and other messages, invoke automated responses to protect / preserve assets. The risk results may also be grouped in risk valuation datasets.

The present application relates to the field of photovoltaic power generation, and particularly relates to a local climate regulation and power generation gain method and system for a desert photovoltaic power station; environmental data and equipment state data in an internal area and an upwind area of the photovoltaic power station are collected in real time to generate standardized data; the standardized data is input into a coupled climate model for prediction to generate a prediction result; based on the prediction result, a global optimization target of maximizing a net present value of power generation gain is considered, power generation loss avoided by intervention, energy cost consumed by intervention, and loss cost of intervention actions on equipment life are comprehensively considered to generate an optimal control scheme; according to the optimal control scheme, a distributed array of environmental regulation equipment is dispatched to perform physical intervention; the present application realizes a change from passive operation and maintenance to active regulation and control of the desert photovoltaic power station, and significantly improves power generation efficiency and reduces operation and maintenance cost.

This invention provides a method, apparatus, and electronic device for downscaling and correcting future meteorological data. The method includes: acquiring historical raster data and corresponding regional measured meteorological data from various stations, verifying the reliability of the historical raster data to determine a historical measured baseline dataset; acquiring historical meteorological data and future scenario meteorological data from multiple global climate models, as well as geographic information data of the study area; downscaling the historical meteorological data and future scenario meteorological data from the global climate models based on the geographic information data; and performing multi-model ensemble processing and bias correction on the historical meteorological data and future scenario meteorological data from the global climate models to obtain downscaled and corrected future scenario meteorological data. This invention improves the reliability of the correction baseline and solves the problem of insufficient spatial downscaling accuracy when processing global climate model data.

The present application relates to renewable energy assessment and weather forecasting technical field, especially to a kind of wind and light resource fine estimation method based on numerical simulation and observation constraint. Global climate model driving field set is constructed, and the optimal GCM combination is screened out;Regional climate model framework is constructed;High spatio-temporal resolution simulated wind speed and solar radiation data are obtained;Generate kilometer level, hourly wind and light resource data set of history and future. The present application provides a kind of wind and light resource fine estimation method based on numerical simulation and observation constraint, by introducing high resolution, multi-source fusion ground surface wind speed and solar radiation long sequence historical observation data, using quantile mapping method to carry out systematic deviation correction to simulation result, realize statistical downscaling processing under the observation data constraint, finally obtain the wind resource data with 5 kilometers horizontal resolution, so as to significantly improve the high spatio-temporal resolution information capture ability to future wind resource change.

This invention discloses an adaptive localization method based on an ensemble paleoclimate data assimilation framework. It constructs an observation density field and updates the localization radius of model grid points. If a proxy record is located within its localization radius, its weight is calculated using a Gaspari-Cohn fifth-order polynomial. If the proxy record is located outside its localization radius, the correlation information between the climate model grid points and the proxy record is calculated. If the correlation information meets a correlation threshold, the weight of the proxy record is calculated using the correlation information; otherwise, the weight of the proxy record outside the localization radius is calculated using a Gaspari-Cohn fifth-order polynomial. Combining the statistical correlation information within the reconstruction period, a final mixed weight matrix is ​​obtained, which is then used to adjust the covariance matrix. While ensuring that each model grid point retains a certain amount of observational influence, statistical correlation is used to reduce spurious teleconnections and improve reconstruction results.

Embodiments relate to computer systems and methods for computer models and scenario generation. The system involves generating integrated climate risk data using a Climate Risk Classification Standard hierarchy that maps climate data and multiple risk factors to geographic space and time. A computer model involves risk factors modeled as graphs of nodes, each node corresponding to a risk factor and connected by edges or links. The nodes of the graph create scenario paths for the model. A hardware processor populates the graphs of nodes using a machine learning, natural language processing and expert judgement systems. The system automatically generates multifactor scenario sets using the scenario paths for the climate model to compute the likelihood of different scenario paths for the computer model.

This invention provides a method, medium, and system for constructing a predictive model for suitable habitats of the peach aphid, belonging to the field of peach aphid prediction technology. This invention obtains peach aphid distribution points from the Global Biodiversity Information Facility and obtains an effective set of coordinate points through spatial filtering. It then selects bioclimatic variables from the WorldClim database and obtains an effective set of variables through variance inflation factor screening and principal component analysis. The above data is input into the model to output the suitability probability. The uncertainty of the multi-climate model set is then modeled as a continuous probability density field to generate a probability suitability map. Simultaneously, the suitability probability and the effective variable set are input into a maximum entropy model and combined with elastic network regularization to output a habitat suitability index. Finally, spatial cross-validation is used to evaluate the accuracy, and the suitability zone classification and spatiotemporal change identification are completed based on the Jenks natural discontinuity classification method. This invention solves the technical problem that climate uncertainty cannot be continuously propagated in AI-driven species suitability zone prediction.

The present application provides a data assimilation method and system for predicting seawater quality change by using tide station records, and relates to the technical field of marine observation. The method comprises the following steps: selecting a tide station; calculating the global land mass migration change to obtain the sea level fingerprint effect; extracting the dynamic sea level change of the climate model and interpolating the dynamic sea level to the tide station record data; establishing an observation equation according to the selected tide station and using a sparse matrix to associate the state quantity and the observation quantity; introducing a random variable at each tide station to make up for the deficiency of the climate model in simulating the local dynamic sea level change; determining the initial time state of the tide station, optimizing and adjusting the constraint parameters of the state quantity and the correlation coefficient between adjacent tide stations; and based on Kalman filtering and smoothing, estimating the optimal state quantity and predicting the global seawater quality increase. The present application improves the accuracy of seawater quality estimation and realizes the coincidence and compatibility of model prediction and tide station observation data.

The application discloses a kind of multi-mode wind energy resource month scale prediction revision method and system based on U-Net, belong to wind energy resource evaluation and climate numerical prediction cross technical field, for the month scale wind speed of climate mode output is revised.The method constructs climatic wind speed field using ERA5 reanalysis, calculates month scale 10m wind speed anomaly as revision benchmark;Obtain the month scale historical return data of multiple dynamic climate models, extract 10m wind speed and multiple layers meteorological elements, uniform interpolation and standardization, form sample set.Based on sample set, the U-Net revision model with encoding and decoding structure is constructed, and the feature combination and hyperparameter are optimized through cross-validation, to learn the nonlinear mapping from multi-mode prediction field to ERA5 anomaly field.The optimal model is used to revise the future month scale prediction, to generate wind speed products with more similar spatial structure and amplitude distribution to observations, to provide high credible wind energy climate information for wind power planning, power generation planning and power grid dispatching.