29 results about "Weather patterns" patented technology

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.

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 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.

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.

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 present invention relates to the technical field of deep learning, and specifically to a new energy photovoltaic power prediction system and method based on deep learning. It includes obtaining historical power data and meteorological data of a photovoltaic site, and performing power prediction for the current time series through a single-site power prediction model; capturing the spatial correlation between sites through establishing a multi-site joint prediction model to predict the power of the photovoltaic site; establishing a weather pattern recognition method according to the meteorological data to distinguish normal weather patterns and sudden weather patterns; establishing an adversarial weather-power joint prediction model to process multi-site power collaborative prediction. The present invention improves the site power prediction efficiency through online learning and transfer learning for single-site power prediction and multi-site joint prediction; through simulating extreme and severely changing weather conditions for adversarial training to establish an adversarial weather prediction network, and then performing joint prediction of weather and power, the accuracy of the prediction model in a harsh environment is improved.

The present invention discloses a method for constructing typical wind-solar scenarios containing meteorological data based on density peak-FCM. The method for constructing typical wind-solar scenarios includes the following steps: S1: Obtain the data of historical numerical weather forecasts and the historical annual measured output data of wind farms and photovoltaic power stations, and construct an original data set matrix; S2: Perform dimensionality reduction processing on the original data set matrix constructed in S1, and construct a weather feature matrix after dimensionality reduction; S3: According to the density peak optimized fuzzy C-means clustering algorithm, perform clustering processing on the weather feature matrix after dimensionality reduction obtained in S2; S4: Classify the historical output data of wind farms and photovoltaic power stations according to the results of weather pattern recognition in S3, and then fit to obtain typical daily output scenarios of wind and light under different weather patterns. This method can not only retain the original data features to the greatest extent by performing dimensionality reduction processing on various meteorological feature data, but also avoid data redundancy and improve calculation efficiency.

The invention discloses a wind-light-water cooperative power prediction method capable of adapting to extreme weather changes. The method comprises the following core steps: defining a state space containing a plurality of weather modes based on historical hydro meteorological data and power data; dividing historical data into a plurality of data subsets with state tags based on the state space; for each weather state, independently training an exclusive power prediction sub-model by using the corresponding data subset; when real-time power prediction is carried out, firstly, the weather state of the current moment is identified, and the next weather state is pre-judged in combination with the state transition probability matrix; and dynamically calling the sub-models of the current state and the pre-judged next state, and carrying out weighted fusion on the prediction results of the current state and the pre-judged next state to generate a power prediction result. According to the method, through a dynamic switching mechanism of'first classification and second prediction ', extreme weather is effectively dealt with, and the robustness and accuracy of prediction are greatly improved.

The invention discloses a quick interaction method and system based on a vehicle control button and electronic equipment, and relates to the field of man-machine interaction, and the method comprises the steps: starting up to enter a central control screen desktop view angle, and screen display comprises a scene mode, a weather mode and a driving preview vehicle control quick interaction button panel; the scene mode provides default scene and extremely simple scene options and is used for switching a current display scene; the weather mode comprises a weather module and a time module, the weather module provides options of sunny days, snowy days and rainy days, the time module provides options of morning and evening, daytime, evening and night, and weather and time combination is realized by combining the options of the weather module and the time module in pairs; driving preview provides six preview modes of narrow road passing, door opening early warning, lane departure, driving lever lane changing, traffic light passing and high-speed cruising, and is used for displaying vehicle states and surrounding environments under different road conditions; the vehicle control quick interaction button panel comprises a vehicle control button; and completing mode switching, module control or / and option activation according to the touch control of the vehicle control button.

The computer-implemented method may include identifying initial color data for an initial coating to be applied to a target object, the initial color data identifying (i) a coating having multiple components and (ii) colorimetric data for the coating as applied; identifying weathered color data for the initial coating on the target object; determining altered colorimetric data between the initial color data and the weathered color data; processing the altered colorimetric data through one or more machine learning algorithms; receiving an end-user selection provided through a graphical user interface of a new coating having a different multiple components; determining a predicted weathering morphology of the new coating upon application of multiple weathering exposure conditions using the one or more machine learning algorithms; and displaying data corresponding to the predicted weathering morphology of the new coating.

The invention discloses a real-time power prediction method and apparatus, an electronic device and a storage medium, and through the application, real-time numerical monitoring data and sky image data of a photovoltaic power station can be fused at the same time to generate a situation vector comprehensively reflecting an environment state, so that a prediction model can capture weather dynamics more accurately; a real-time correction factor is generated through the deviation between the first predicted power and the actually measured power, input parameters of a physical model are dynamically corrected, and the situation that the theoretical deviation and the actually measured deviation are too large under sudden weather change is avoided; dynamic weighted fusion is carried out on the two predicted powers, the nonlinear fitting capability of a data-driven model and the theoretical support advantage of a physical model are combined, the limitation of the coverage range of training data is broken through, and unexperienced weather modes are quickly responded; the technical effects of improving the accuracy of power prediction in sudden weather, reducing the prediction error of an unexperienced weather mode, improving the precision and reliability of the final prediction power, and providing accurate data support for intelligent power grid dispatching and energy optimization configuration are achieved.

The present invention discloses a photovoltaic power prediction method, which relates to the technical field of smart grids. The method includes collecting and preprocessing data from multiple data sources to form a multi-modal data set; constructing a radiation transfer model to calculate the solar radiation intensity on the surface of photovoltaic modules; constructing a photovoltaic power prediction model based on historical data to capture the non-linear relationship between weather patterns and photovoltaic power output, and outputting the predicted photovoltaic power; training the photovoltaic power prediction model using the historical data set, and taking the solar radiation intensity as an auxiliary input to the model; collecting a real-time multi-modal data set, combining the real-time solar radiation intensity and inputting it into the trained photovoltaic power prediction model to perform real-time photovoltaic power prediction; dynamically adjusting the prediction strategy according to the error of the real-time prediction value and optimizing the model parameters; according to the photovoltaic power prediction result, combining the real-time demand of the power grid and the state of the energy storage system, efficiently allocating and managing electric power resources.

The system according to the embodiment aims to optimally adjust the orientation and angle of solar panels based on weather data, and to perform anomaly detection and power supply management during disasters. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a control unit, a detection unit, and a charging management unit. The data collection unit collects weather data. The analysis unit analyzes the data collected by the data collection unit and predicts future weather patterns. The control unit optimally adjusts the orientation, angle, and area of ​​the solar panels based on the weather patterns predicted by the analysis unit. The detection unit analyzes images of the solar panels in real time and detects abnormalities. The charging management unit charges EVs using electricity generated by solar power and manages power supply to disaster areas and base stations in the event of a disaster.

The application discloses a photovoltaic cluster power short-term prediction method based on space-time generalized weather pattern division, and belongs to the technical field of photovoltaic cluster power short-term prediction. The method comprises the following steps: acquiring the historical power of all power stations in a photovoltaic cluster, the total historical power of the cluster, the D+1 day forecast irradiance and the longitude and latitude data; dividing all the power stations into n sub-regions according to the longitude and latitude data; acquiring the generalized weather pattern of the cluster on the predicted day; training a graph convolutional neural network based on the D+1 day forecast irradiance of all the power stations, the D day historical power and the calculated Kendall coefficient; obtaining power prediction models under three generalized weather patterns respectively; inputting the feature matrix and the adjacency matrix of the predicted day into the power prediction model under the corresponding generalized weather pattern according to the generalized weather pattern of the cluster on the predicted day; and obtaining the prediction result. The application significantly improves the power prediction accuracy and the model generalization ability.

The application provides a kind of based on deep learning regional power grid load prediction method and system, regional power grid load prediction method includes: integration real-time power consumption data and historical power consumption database constructs dynamic dataset;Using time series analysis model extracts current cycle power consumption characteristics and carries out similar judgment with historical same period data;If it is judged that it is not similar, obtain the first position information, judge whether the current region satisfies the first electricity condition;Combining weather forecast matching historical similar weather mode, extract the power consumption characteristic data and power generation characteristic data in future second cycle and associated cycle;Judge whether the current region satisfies the load requirement in future second cycle;If it is judged that it is not satisfied, the power of adjacent region needs to be adjusted;If it is judged that it is satisfied, continue routine monitoring, and send power borrowing early warning to adjacent region;If it is judged that it is similar, it indicates that the load condition of the current region is stable.The application improves the intelligent degree of power grid load prediction.