40 results about "Weather patterns" patented technology

The invention relates to the technical field of motion trail simulation tracking, in particular to a wind-blown snow motion trail simulation tracking method based on a wind-blown snow numerical model, which comprises the following steps of: extracting a spatial coupling relationship among local meteorological variables, and constructing a meteorological region division basis with higher discrimination capability, so that region division does not depend on univariate layering any more; therefore, the recognition capability of a local strong disturbance area is improved, a meteorological time sequence is segmented and analyzed through a convolutional neural network, a nonlinear time sequence evolution rule among meteorological variables is captured, an interactive mapping relation between meteorological changes and trajectory changes is constructed, the response sensitivity of a prediction model to sudden weather forms is enhanced, and the prediction accuracy is improved. According to the method, weighted evaluation is carried out on multiple groups of model prediction results through a random forest, a final prediction path is optimized according to historical error distribution, structured tracking of error sources is realized, and adaptability and stability of the prediction results are improved.

The invention discloses a short-term power prediction method, device and equipment for a photovoltaic power station, a medium and a product. The method comprises the following steps: acquiring historical actual environment data and historical actual power data of multiple sources, and extracting a feature vector of each data source; an attention mechanism is adopted to adaptively distribute the corresponding feature weight of each data source according to the importance degree of the environment variable; performing weighted summation on the feature vectors according to the feature weights to generate fused feature vectors, and performing feature selection based on the fused feature vectors to obtain an optimized feature set; training a hybrid prediction model combining a classification algorithm and a time series data prediction algorithm according to the optimized feature set; the classification algorithm is used for judging a weather mode, and the time series data prediction algorithm is used for performing power prediction based on the weather mode; and acquiring environment prediction data, and generating a power prediction result by using the trained hybrid prediction model. Therefore, more accurate and stable power prediction is provided for nonlinear offshore data with high-dimensional and high-frequency fluctuations.

The invention relates to the technical field of deep learning, in particular to a new energy photovoltaic power prediction system and method based on deep learning. Comprising the steps of obtaining historical power data and meteorological data of a photovoltaic station, and performing power prediction of a current time sequence through a single-station power prediction model; building a multi-site joint prediction model to capture the spatial correlation between sites so as to predict the power of the photovoltaic sites; establishing a weather mode identification method according to the meteorological data to distinguish a normal weather mode and a sudden weather mode; and establishing an adversarial weather-power combined prediction model to process multi-site power collaborative prediction. According to the method, single-station power prediction and multi-station joint prediction are carried out through online learning and transfer learning, so that the station power prediction efficiency is improved; by simulating extreme and violently changing weather conditions, adversarial training is carried out to establish an adversarial weather prediction network, and then weather and power combined prediction is carried out, so that the accuracy of the prediction model in a severe environment is improved.

The invention discloses an atmosphere chemical weather coupling rapid updating circulation method and system, and the method comprises the steps: collecting original ground aerosol observation data in a preset region, and carrying out the preprocessing of the ground aerosol observation data, the aerosol observation data being PM2.5 and PM10; preparing atmospheric chemical forecast data of a chemical weather mode, and performing atmospheric chemical background field preprocessing on the forecast data to obtain a chemical background field based on a chemical weather coupling assimilation system; updating an atmospheric chemical initial field according to the analysis field; based on a CMA-MESO rapid updating circulation process, an atmosphere chemical weather coupled rapid updating circulation system is constructed.

The invention relates to a PEM water electrolysis hydrogen production multi-tank series-parallel optimization method, which comprises the following steps: S1, obtaining photovoltaic power generation data, and carrying out data preprocessing; s2, dividing the photovoltaic power generation daytime data into a sunny day and a non-sunny day according to a weather mode by adopting a K-means clustering algorithm, and calculating the average power of different weathers; s3, constructing a scheduling strategy; s4, performing global search and optimization by adopting an improved particle swarm optimization (PSO) algorithm to find an optimal PEM scheduling strategy; s5, recording the operation time and the state switching frequency of each PEM electrolytic cell, and evaluating the generated PEM scheduling strategy; according to the method, the influence of weather on photovoltaic power generation is combined with PEM electrolytic cell distribution and scheduling through a K-means clustering algorithm, an optimal coupling control strategy, multi-target optimization and operation time management are designed, the switching frequency of the PEM electrolytic cells is remarkably reduced, equipment abrasion and maintenance cost are reduced, and the efficiency of PEM water electrolysis hydrogen production is improved.

Provided is a digital twin model-based method for predicting the power generation amount of a photovoltaic power station. The method includes the steps of: acquiring location information and weather information of a photovoltaic power station; generating a digital twin model for cloud movement simulation based on information of a region in which the photovoltaic power station is located and a weather condition corresponding to the information of the region; modeling cloud movement information by inputting regional weather pattern information to the digital twin model; acquiring future time-based solar irradiance prediction information by inputting the modeled cloud movement information into a pre-trained artificial intelligence model; and predicting the amount of power generation of the photovoltaic power station by calculating thermal information of a photovoltaic panel corresponding to the solar irradiance prediction information.

The invention discloses a photovoltaic cluster power short-term prediction method based on time-space generalized weather mode division, and belongs to the technical field of photovoltaic cluster power short-term prediction. Comprising the following steps: acquiring historical power of all power stations in a photovoltaic cluster, total historical power of the cluster, forecast irradiance of D + 1 days and longitude and latitude data, dividing the power stations into n sub-regions according to the longitude and latitude data of all the power stations, and acquiring a generalized weather mode of the cluster on a predicted day, training the graph convolutional neural network based on the D + 1 day forecast irradiance of all power stations, the D day historical power and the calculated Kendall coefficient, and respectively training to obtain power prediction models in three generalized weather modes; and inputting the feature matrix and the adjacent matrix of the predicted day into a power prediction model in a corresponding generalized weather mode to obtain a prediction result. According to the method, the power prediction precision and the model generalization ability are remarkably improved.

Solar panel system and method for self-cleaning The disclosure covers a solar panel system (10) comprising: at least one solar panel (1); a self-cleaning mechanism (2) for the at least one solar panel (1); one or more panel sensors (4) configured to measure at least one performance parameter linked to an energy-production efficiency of the solar panel (1); environmental sensors configured to measure environmental data; a data acquisition module (6) configured to obtain the at least one performance parameter and the environmental data as a function of time; a control system (8) for controlling the self-cleaning mechanism (2), the control system (8) comprising: one or more processor configured to: predict a future weather pattern based on the obtained environmental data; and determine, based on the at least one performance parameter and the predicted future weather pattern, a time tA at which to activate the self-cleaning mechanism (2). The present disclosure also relates to a computer-implemented method for determining when a cleaning mechanism (2) of a solar panel (1) is to be activated.

The invention provides a photovoltaic power generation power climbing prediction and early warning method based on multi-dimensional observation information, and the method comprises the steps: obtaining multi-source data, carrying out the time-space registration and precision correction, and generating a standardized multi-dimensional observation data set; according to the standardized multi-dimensional observation data set, identifying a weather mode and generating a weather mode classification data set and an extreme weather label set; according to the weather mode classification data set, constructing a mapping model of weather modes and earth surface irradiance, and generating a power prediction reference data set; generating a power station group space correlation topological data set and a dynamic weight data set according to the power prediction reference data set and the geographic position of the photovoltaic power station; and calculating a power climbing risk index, and generating a multi-dimensional observation information-based large-scale photovoltaic power generation power climbing prediction and early warning data set under the extreme weather. According to the invention, accurate prediction and timely early warning of the power climbing risk of the large-scale photovoltaic power station group under the extreme weather condition are realized.

The present application relates to a kind of PEM electrolytic water hydrogen production multi-tank hybrid optimization method, comprising the following steps: S1, the data of photovoltaic power generation is obtained, and data preprocessing is carried out;S2, using K-means clustering algorithm, the daytime data of photovoltaic power generation is divided into sunny day and non-sunny day according to weather mode, and the average power of different weather is calculated;S3, scheduling strategy is constructed;S4, using improved particle swarm optimization PSO algorithm for global search and optimization, find the optimal PEM scheduling strategy;S5, record the running time of each PEM electrolytic tank and state switching frequency, and evaluate the generated PEM scheduling strategy;The present application combines the influence of weather on photovoltaic power generation with PEM electrolytic tank allocation scheduling by K-means clustering algorithm, designs optimal coupling control strategy, multi-objective optimization and running time management, significantly reduces the switching frequency of PEM electrolytic tank, reduces equipment wear and maintenance cost, improves the efficiency of PEM electrolytic water hydrogen production.

A multi-weather outdoor RGBD image generation method, system and device, and a storage medium, the method comprising: step S1: obtaining an indoor RGBD image, detecting a target object in the indoor RGBD image, and determining an illumination direction according to the target object; s2, determining the shape and position of a light spot according to the illumination direction; s3, adjusting the overall brightness of the target object and / or the background according to the weather mode; and S4, determining a brightness distribution form according to the depth value and the brightness of the target object and the shape of the light spot, intercepting a part on the brightness distribution form to generate the light spot, and superposing the light spot to the position. According to the invention, an outdoor scene image which better accords with reality can be generated.

The invention provides a regional power grid load prediction method and system based on deep learning. The regional power grid load prediction method comprises the following steps: integrating real-time power consumption data and a historical power consumption database to construct a dynamic data set; using a time sequence analysis model to extract current period power consumption characteristics and performing similarity judgment on the current period power consumption characteristics and historical same period data; if not, acquiring first position information, and judging whether the current region meets a first electricity charge condition or not; matching historical similar weather modes in combination with weather forecast, and extracting power utilization characteristic data and power generation characteristic data in a second period and an associated period in the future; judging whether the current region meets the load requirement in the second period in the future; if not, electric quantity allocation of the next region is needed; if yes, daily monitoring continues to be carried out, and electricity borrowing early warning is sent to an adjacent area in advance; if yes, the load condition of the current region is stable. According to the invention, the intelligent degree of power grid load prediction is improved.

The present invention comprises the deployment of a product known as RubisCO Climate Vaccine® or simply RCV which contains a specialised adapted culture of psychrophile algae and accelerators. This RCV is inoculated into targeted tropospheric clouds. The cumulatively large air-liquid optical interface area of clouds enables conditions for potential exponential photosynthetic algal growth and replication during which carbon dioxide is processed and oxygen released directly in the troposphere. Optimal inoculation sites are determined by altitude, humidity, temperature, weather patterns and predicted direction and duration of travel for precipitation, The biomass produced in this process is intended to be precipitated over mid-ocean where it is sequestered within natural ocean food chains or sedimented as detritus essentially permanently. This nature-based solution through repeated deployments offers a feasible scalable pathway toward potential climate change reversal within a decade with minimal or no adverse environmental impact.

The present application relates to the technical field of motion trajectory simulation tracking, in particular to a wind-blowing snow motion trajectory simulation tracking method based on a wind-blowing snow numerical model, in the present application, the spatial coupling relationship between local meteorological variables is extracted, a more discriminant meteorological regional division basis is constructed, the regional division is no longer dependent on single variable stratification, thereby the identification ability of local strong disturbance area is improved, the meteorological time series is analyzed by convolution neural network, the nonlinear time series evolution law between meteorological variables is captured, and the interactive mapping relationship between meteorological change and trajectory change is constructed, the response sensitivity of the prediction model to sudden weather patterns is enhanced, the random forest is used for weighted evaluation of the prediction results of multiple groups of models, the final prediction path is optimized according to the historical error distribution, the error source is tracked in a structured manner, and the adaptability and stability of the prediction result are improved.

The invention relates to the technical field of power grid thunder early warning, in particular to a power grid thunder early warning analysis method and system based on AI, and the method comprises the following steps: based on real-time meteorological data, through continuously monitoring weather mode changes, recognizing thunder frequency information at key time points, and correspondingly adjusting a time window of data collection for each time point according to a recognition result; and matching the lightning activity change to generate a lightning time dynamic adjustment result. According to the invention, through real-time monitoring of meteorological data and dynamic adjustment of a time window of data acquisition, lightning early warning accuracy and response speed are significantly improved, continuous time sequence analysis is adopted, and adaptive capacity to rapid change of lightning activity is enhanced, so that an early warning system can be adjusted in real time to match dynamic change of lightning, and lightning early warning accuracy and response speed are improved. Through refined thunder and lightning trend prediction, thunder and lightning events can be predicted more accurately, a power grid operation strategy is optimized accordingly, and the safety, stability, power grid guarantee and user safety of a power grid are greatly improved.

The invention relates to a meteorological element correction method for an electrified railway catenary, and the method comprises the steps: obtaining the topographic data of an electrified railway catenary region, and determining the topographic parameters and topographic types of the electrified railway catenary region according to the topographic data; for each terrain category, determining a fitting coefficient of a meteorological correction model according to the terrain parameters and historical meteorological observation data of the electrified railway overhead line system area; the meteorological correction model is used for determining meteorological correction according to the fitting coefficient and the terrain parameters; in response to the risk prediction request for the current area, acquiring a meteorological simulation value of the current area; and correcting the meteorological simulation value according to a target meteorological correction model corresponding to the terrain category of the current region to obtain meteorological prediction data of the current region. According to the technical scheme provided by the invention, the problem that an existing numerical meteorological mode is insufficient in forecasting precision in a fine-scale topographic region is solved, and the accuracy and practicability of risk prediction of the overhead line system are improved.

This invention belongs to the field of air quality numerical forecasting technology, and relates to a method and system for improving the performance of medium- and long-term air quality forecasts. The method includes: determining the weather patterns for each sub-region and each buffer zone at each time period; determining the optimal parameterization scheme combination under different weather patterns; determining the future weather patterns for each time period within a future timeframe; determining the ensemble forecast field for the previous time period; determining the ensemble forecast field for the next time period; determining the ensemble forecast field for all time periods in the region; determining the ensemble forecast field for all time periods in all sub-regions and buffer zones; determining the temporal and spatial coupled meteorological field of the inner nested regions of each sub-region; determining the meteorological field of the large region; and determining the meteorological field of each nested region. It utilizes weather classification, multiple initial field perturbations, and multi-regional, multi-time period forecast result coupling techniques to improve the performance of medium- and long-term air quality forecasts by enhancing the medium- and long-term forecasting performance of meteorological models.

The invention discloses a method for improving sand and dust numerical forecasting through assimilation-aerosol-cloud-radiation, and the method comprises the steps: building a sand and dust storm forecasting mode, calculating sand and dust optical parameters based on a spherical particle extinction theory, and achieving the bidirectional feedback of sand and dust to weather; a sand and dust aerosol-cloud interaction ice nucleus nucleation mechanism is given, a sand and dust CCN type aerosol homogeneous freezing process is realized, a radiation variable temperature rate is calculated and fed back to a weather mode power process, and bidirectional feedback of weather-driven sand and dust and influence of sand and dust on weather is formed; constructing an ensemble forecasting module, performing time and space related disturbance on a meteorological initial field, a boundary condition and an aerosol initial concentration, and performing ensemble forecasting after a mode is input; and establishing an ensemble assimilation analysis correction module, constructing a background error covariance matrix by utilizing mode ensemble forecasting, disturbing observation at an analysis moment, and assimilating and correcting the initial concentration of the mode aerosol based on a localized ensemble Kalman filtering method to obtain a forecasting result.

An object of the invention is to provide a technique capable of accurately determining the amount of power to be exchanged between locations, even when the amount of power generated by a photovoltaic system varies greatly depending on the weather. A power exchange system according to the invention searches for a power transmission source location in a weather pattern where the variation in the power generation amount of a solar battery power generation system is small, and transfers an excess amount of power at the power transmission source location to a power transmission source location in a weather pattern where the variation in the power generation amount is large.

The invention discloses a photovoltaic power generation power prediction method based on similar day clustering and an EMD-WT-ISSA-LSTM combination model, and the method comprises the steps: screening meteorological factors with high correlation with photovoltaic power generation power based on a Pearson's correlation coefficient, dividing a weather mode through employing an unsupervised clustering algorithm, and obtaining similar day data sets corresponding to a sunny day, a cloudy day and a rainy day; the method comprises the following steps: decomposing meteorological data into characteristic components through modal decomposition, and denoising and reconstructing through wavelet transform to obtain denoised data; tent chaotic mapping is adopted to initialize the population, a dynamic early warning value which is nonlinearly decreased along with the number of iterations is introduced, and when the fitness value is lower than a preset threshold value, a Levy flight strategy is triggered to adjust a search behavior so as to construct and obtain an improved sparrow search algorithm; performing joint optimization on the hidden layer size, the time step number and the learning rate of the LSTM by using the improved sparrow search algorithm to obtain an optimized LSTM model; and inputting the de-noised data into the optimized LSTM model, and outputting a photovoltaic power generation power prediction result.

1. Name of the design product: Graphical user interface for weather mode for electronic devices. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5.Purpose of the graphical user interface: an operating interface for viewing weather conditions. 6. Description of the changing states of the graphical user interface: When entering the weather mode, the main view is presented. When clicking the "Future Weather" button in the main view, the interface changing state diagram 1 is presented. When the time becomes night, the interface changing state diagram 1 becomes dark and the interface changing state diagram 2 is presented.

Methods, systems, and computer readable media for predictively determining a risk of damage to a property are provided. To determine the risk, a high resolution virtual model of a region that includes the property is obtained. The virtual model is imported into a simulation environment. One or more of the simulation parameters are set based on historic weather data for the region. For example, each parameter may be associated with a probability distribution derived based on the historic weather data that is sampled prior to executing the simulation. One or more simulations are executed in accordance with the sampled inputs to simulate the likely weather patterns the property will experience. The result of the simulation is analyzed to determine the predicted risk of damage to the property.

Methods, systems, and computer readable media for predictively determining a risk of damage to a property are provided. To determine the risk, a high resolution virtual model of a region that includes the property is obtained. The virtual model is imported into a simulation environment. One or more of the simulation parameters are set based on historic weather data for the region. For example, each parameter may be associated with a probability distribution derived based on the historic weather data that is sampled prior to executing the simulation. One or more simulations are executed in accordance with the sampled inputs to simulate the likely weather patterns the property will experience. The result of the simulation is analyzed to determine the predicted risk of damage to the property.

This invention discloses a chemical weather model forecasting method and system based on atmosphere-chemistry bidirectional coupling. The method includes full-process variable registration, writing grid pointer variables to obtain grid static ground data, extracting key physical parameters, calculating the dynamic transport of the tracer array in the main integration program and transmitting it to the CHEM driver, calling the CHEM driver in the main integration program, and simultaneously transmitting the CMAMeso meteorological field, the tracer array, the physical parameters, and the grid static ground data to CUACE for atmospheric chemical process calculation. The atmospheric chemical process calculation results are then transmitted back to the main integration program to achieve online atmosphere-chemistry coupling. The method establishes real-time calculation of key aerosol radiation parameters, performs real-time cloud droplet activation to obtain cloud droplet number and concentration, establishes an aerosol-cloud interaction mechanism to output cloud parameters, updates the radiation transport scheme, and performs numerical forecasts of environmental meteorological and weather elements. This method can improve the accuracy of chemical weather model forecasts and also has good physical interpretability.

A weather depiction system for an aircraft is disclosed. A radar is configured to scan a surrounding environment of the aircraft and provide weather data. An aircraft computing device is configured to: detect weather patterns using the weather data, receive a flight trajectory of the aircraft from a flight management system (FMS), compare the flight trajectory to an altitude of each of the weather patterns, identify the weather pattern as relevant or non-relevant based on the comparison, and present symbols corresponding to the relevant weather patterns on the weather display and exclude symbols corresponding to the non-relevant weather patterns on the weather display.

The invention discloses a chemical weather mode forecasting method and system based on atmosphere-chemical bidirectional coupling, and the method comprises the steps: carrying out the whole-course variable registration, writing in a grid pointer variable to obtain grid static ground data, extracting key physical parameters, calculating the power of a tracker array in a main integral program, transmitting the power to a CHEM driver, calling the CHEM driver in the main integral program, and carrying out the calculation of the power of the tracker array in the main integral program. Meanwhile, a CMAMeso meteorological field, the tracker array, the physical parameters and the grid static ground data are transmitted to CUACE for atmospheric chemical process calculation, and an atmospheric chemical process calculation result is transmitted back to the main integral program to realize atmospheric-chemical online coupling; the method comprises the steps of establishing aerosol key radiation parameter real-time calculation, performing cloud droplet real-time activation to obtain cloud droplet number concentration, establishing an aerosol-cloud interaction mechanism to output cloud layer parameters, updating a radiation transmission scheme, and performing numerical forecasting of environmental meteorology and weather elements. And meanwhile, the method has good physical interpretability.

A method for constructing a pre-fabricated component of a building, including: configuring a pre-fabricated component of a building based on energy informed modelling; wherein the pre-fabricated component is able to be assembled with at least one other pre-fabricated component into the building at a final site for the building. A method for constructing a building, including: performing site analysis to determine at least one environmental condition; and optimizing at least one pre-fabricated component of the building based on energy informed modelling based on one or more of historical data of a location of the building, weather patterns of the location, climate data of the location, temperature data of the location, solar data of the location, orientation data of the location, occupancy rate of the building, building insulation data, ventilation and infiltration data, exterior fenestration, shades implementation, or pre-fabricated component configuration.

The invention discloses a meteorological big data prediction system based on dynamic weight adjustment, and belongs to the technical field of meteorological forecasting. The invention aims to solve the problem that the existing ensemble forecasting method is difficult to foreseeably deal with the sharp drop of the performance of a numerical model in a specific weather mode due to static or lagged weight distribution. The method comprises the following steps: firstly, in an off-line stage, constructing a physical process model performance causal atlas for revealing internal association between a specific physical process and model performance reduction by mining historical meteorological data and model forecast errors; and training a time-space diagram neural network on the basis of the map labeling sample, so as to identify a physical precursor causing the failure of the model. According to the invention, by introducing physical causal and precursor pre-judgment, the transformation from post-assessment to pre-warning is realized, so that the weight distribution is more physically interpretable and prospective, and the accuracy and reliability of ensemble forecasting in complex and extreme weather events are remarkably improved.

The invention discloses a distributed photovoltaic ultra-short-term prediction method based on satellite data. The distributed photovoltaic ultra-short-term prediction method comprises the following steps: multi-source heterogeneous data collection and synchronous fusion: collecting heterogeneous data by using multi-source equipment; cloud cluster dynamic capture and high-resolution data interpolation: constructing a cloud motion model in combination with an optical flow method and a CNN to perform motion estimation on a satellite cloud picture sequence to generate a cloud cluster moving speed field, generating satellite data by adopting TimeGAN, and reconstructing original data through cGAN; extreme weather emergency response and dynamic correction: introducing a historical similar weather mode library to perform power prediction compensation through mode matching to reduce prediction deviation in extreme weather; multi-model collaborative prediction and mechanism optimization: taking the LSTM as the core, and improving the global optimization efficiency by combining PSO optimization hyper-parameters with multi-group PSO parallel search to realize PSO-LSTM cloud cover prediction; and outputting a prediction result and evaluating an effect: generating a probability prediction interval by adopting a quantile regression network, and quantitatively evaluating the prediction effect in combination with residual analysis.

The invention provides an air-conditioning efficient refrigerating machine room load prediction method, and belongs to the technical field of refrigerating machine room load prediction. According to the method, the annual complex weather mode and the unit operation mode are classified through the set clustering method, so that the method is simplified and convenient to regulate and control; through respectively considering influence factors of internal and external loads, correlation degree screening is carried out, and the influence factor with the highest correlation degree, especially the influence of solar radiation, is selected, so that the calculation error of the external load in summer is reduced; the load change of the second day is predicted through the weather forecast of the second day, the operation number and operation time of the air conditioning units of the second day are directly regulated, the units are regulated in advance, the operation mode can be continuously optimized and adjusted according to the actual weather condition, manpower and material resources can be reduced, and the method is novel, suitable for different machine rooms and high in practicability. Load distribution of the refrigerating machine room is obtained by integrating and processing various kinds of information such as working information, environment information and weather data of the refrigerating machine room.