687 results about "Weather prediction" patented technology

A system and method for combining the delivery of advertising with weather predictions that are limited in geo-graphical area and time, and hence which are much more precise but also more time sensitive than regular weather forecasts. The present invention is preferably implemented with “nowcasting”.

According to various implementations, a demand response (DR) strategy system is described that can effectively model the HVAC energy consumption of a house using a learning based approach that is based on actual energy usage data collected over a period of days. This modeled energy consumption may be used with day-ahead energy pricing and the weather forecast for the location of the house to develop a DR strategy that is more effective than prior DR strategies. In addition, a computational experiment system is described that generates DR strategies based on various energy consumption models and simulated energy usage data for the house and compares the cost effectiveness and energy usage of the generated DR strategies.

Electric utility storm outage management is performed by determining an interconnection model of an electric utility power circuit, the power circuit comprising power circuit components, determining information indicative of weather susceptibility of the power circuit components, determining a weather prediction, and determining a predicted maintenance parameter based on the interconnection model, the weather susceptibility information, and the weather prediction.

A modeling framework for estimating crop growth and development over the course of an entire growing season generates a continuing profile of crop development from any point prior to and during a growing season until a crop maturity date is reached. The modeling framework applies extended range weather forecasts and remotely-sensed imagery to improve crop growth and development estimation, validation and projection. Output from the profile of crop development profile generates a combination of data for use in auxiliary farm management applications.

The invention provides an ultra-short term wind power station generated power forecasting system, which belongs to the technical field of automatic scheduling of wind power plants. The system comprises a real-time wind measurement data server, a numerical weather prediction server, a wind power station real-time operation server, an ultra-short term forecasting processing server and data acquiring equipment, wherein the numerical weather prediction server is used for acquiring weather prediction data of a near-earth layer at the position of a wind power station; the real-time wind measurementdata server is used for acquiring wind speed data, wind direction data, temperature data, air pressure data and humidity data at the position of a wind measurement tower; the wind power station real-time operation data server is used for acquiring the total generated power data of the wind power station; and the ultra-short term forecasting processing server is used for forecasting the total generated power data of the wind power station at the next moment. The system solves the technical problems that future generated power variation tendency cannot be tracked and multi-step forecasting accuracy is relatively low in the prior art.

The invention provides a Bluetooth smart watch which comprises a watch body and a wireless charger. A Bluetooth module, a wireless charging module, a lithium battery, a small motor, a horn, a microphone, a micro control unit (MCU) and an electrophoretic display (EPD) module are integrated inside the watch body. The wireless charging module is in connection with the lithium battery. The Bluetooth module, the lithium battery, the small motor, the horn, the microphone and the EPD module are respectively connected with the MCU. The wireless charger is connected with the wireless charging module inside the watch body through a wireless electric power transmission technology. A Bluetooth technology is adopted for data interaction between a mobile phone and the watch, a wireless charging technology is utilized for charging the watch, and the watch is small in thickness and multifunctional. Time for watching a message, weather forecast and communication is saved for people, and convenience is brought when the people are in occasions where a call is inconvenient to answer.

The invention discloses a physical prediction method for wind power station power based on a computational fluid mechanics model. The physical prediction method comprises the following steps: establishing a computational fluid mechanics model; performing discretization on wind conditions of a wind power station, and taking the discretized wind as boundary conditions for conditions numerical simulation of the computational fluid to obtain space flow filed distribution of the wind power station at the discretized wind conditions; establishing data base of hub height, wind speed, wind direction and generated power of wind generation sets under the discretized wind conditions; and taking numerical weather prediction parameters as input data, and utilizing the data base to figure out the wind speeds and the wind directions of the wind generation sets so as to figure out the generated powers of the wind generation sets, and accordingly obtain predicted value of the wind power station power. According to the invention, the physical prediction method is applicable to multiple wind power stations, has small calculated amount in the power prediction stage and has short calculation time.

Electric utility storm outage management is performed by determining an interconnection model of an electric utility power circuit, the power circuit comprising power circuit components, determining information indicative of weather susceptibility of the power circuit components, determining a weather prediction, and determining a predicted maintenance parameter based on the interconnection model, the weather susceptibility information, and the weather prediction.

The invention discloses a clustering-analysis-based wind power short-term prediction system and prediction method. The prediction system comprises a short-term prediction server and a real-time data acquisition apparatus; and a power prediction function unit and a prediction database are installed at the short-term prediction server. According to the prediction method, a daily correlation analysis is carried out by using a pearson product moment correlation coefficient to determine consistency of the daily correlation of the wind power and daily similar situation of the available weather information; clustering analysis pretreatment is carried out on historical weather database by using a K mean value clustering method; historical day data similar to a weather feature parameter of a prediction day are selected by using a method using an Euclidean distance as a similarity measure and the data are used as the training samples for neural network prediction model establishment; after training based on the similar samples after clustering, a wind power prediction model based on the cluster analysis is obtained; and the prediction day NWP information is used as input parameter of the model and the wind power is used as the model output, so that prediction power data of the prediction day are obtained.

The invention discloses an energy consumption model prediction method for a battery electric vehicle based on road information and driving style optimization. The energy consumption model prediction method comprises the steps that a vehicle sensor, geographic information software, an electronic map and a weather forecasting system are utilized to acquire vehicle state parameters, road informationparameters and environmental information parameters; according to the acquired parameters, parameter estimation is performed on rolling resistance coefficients, air density and road gradient parameters; a working condition prediction model based on road information and driving style optimization is established to predict the working conditions to enable energy consumption of the predicted workingconditions to be accurately approximated to energy consumption of actual working conditions; and a battery electric vehicle energy consumption prediction model is established for energy consumption prediction, specifically, a battery electric vehicle energy consumption calculation model is established based on a battery electric vehicle performance test, parameter estimation results and working condition prediction results are used as the input of the battery electric vehicle energy consumption calculation model to form the battery electric vehicle energy consumption prediction model, the battery electric vehicle energy consumption prediction model outputs predicted energy consumption, and energy consumption of future path information is predicted.

The invention provides a method for improved building energy usage reduction by computer automation of optimized plant operation times and sub-hourly building energy forecasting to determine plant faults. The invention provides a computer system to derive the NTL, mechanical heat-up (MHL) and mechanical cool-down (MCL) lags and in conjunction with a readily available interval weather forecast, the system can output various signals to indicate optimized start and stop times for heating and cooling equipment. The algorithm to calculate the 15-minute energy forecast is used to indicate out-of-control conditions in the operation of the plant.

A system and method for combining the delivery of advertising with weather predictions that are limited in geographical area and time, and hence which are much more precise but also more time sensitive than regular weather forecasts. The present invention is preferably implemented with “nowcasting”.

The invention relates to a system and method for integrally and intelligently controlling water and fertilizer in a field based on multi-source information fusion. The system comprises a weather forecast inquiry receiving subsystem, a weather real-time data collecting control subsystem, a cloud computing platform, a central control unit, an irrigation and fertilization control subsystem, an irrigation and fertilization state monitoring system and an online fault detection system. The system provided by the invention is an automatic control system integrated with the functions of weather forecast inquiry, crop cloud computing platform inquiry, farmland weather real-time collection, solid fertilizer rapid solution, mother solution real-time monitoring regulation, irrigation and fertilization state monitoring, online fault detection, irrigation and fertilization and remote intelligent control. According to the invention, the factors, such as, weather forecast, cloud computing platform, weather real-time collection and growth vigor of the crops in the growth process can be comprehensively considered, corresponding irrigation and fertilization decisions can be made, and thus precise irrigation and precise fertilization can be accurately realized; and the growth vigor of the crops can be described in real time, and the irrigation and fertilization can be performed according to the growth vigor of the crops.

A method and system for determining the accuracy of a mesoscale weather model comprising at least one processor having at least one input for inputting a preexisting weather model and initial weather data comprising surface level and upper air temperatures and wind conditions, and actually measured surface level and the upper-air level weather conditions; the at least one processor operating to use the mesoscale weather model to generate output data comprising forecasted temperatures, wind conditions, and predicted weather conditions; the at least one processor operating to compare the output data to actually measured data obtained when same or similar initial weather data were present and subsequent resulting temperatures, wind conditions and weather conditions were measured; and the at least one processor operating to generate an accuracy rating reflecting the deviation of temperature, wind conditions and weather conditions predicted by the mesoscale weather model as compared to those actually measured.

The invention relates to a wind electric power prediction method and a device thereof. The method comprises the following steps of: step one: extracting data from SCADA (Supervisory Control and Data Acquisition) relative to a numerical weather prediciton system or a power system, and carrying out smoothing processing on the extracted data; step two: determining input and output of training samples of a least squares support vector machine according to the processed data; step three: initializing relevant parameters of a smallest squares support vector machine and an improved self-adaptive particle swarm algorithm; step four: optimizing model parameters according to an optimization process; step five: acquiring a model of the smallest squares support vector machine according to the optimized parameters; and step six: carrying out prediction according to the model of the smallest squares support vector machine. According to the wind electric power prediction method disclosed by the invention, a modelling process is simple and practical, wind electric power can be rapidly and effectively predicted, and the wind electric power prediction method has an important significance on safety and stability, and scheduling and running of the electric power system, and therefore, the wind electric power prediction method has wide popularization and application values.

The invention discloses an air conditioner sleep mode control method and an air conditioner and belongs to the field of air conditioners and control methods thereof. The air conditioner sleep mode control method and the air conditioner are designed in order to solve the problem that an existing control method is poor in user experience. The air conditioner sleep mode control method includes the steps that when a sleep mode is used for the first time, according to acquired weather forecast information, an initialized sleep mode parameter curve is acquired, when the sleep mode is used before and historic setting information is inquired, the initialized sleep mode parameter curve is acquired according to the historic setting information, and when the sleep mode is used before and historic setting information is not inquired, the initialized sleep mode parameter curve is acquired according to air conditioner preset information; after a control device receives outer adjusting information, modification is conducted on the basis of the initialized sleep mode parameter curve, and the adjusting information is recorded. By the adoption of the air conditioner sleep mode control method and the air conditioner, client experience is good, and use is more convenient.

The invention discloses a wind and photovoltaic generation power prediction system with multiple prediction modes. The system is characterized by comprising a numerical value weather prediction data acquisition module for inputting numerical value weather forecast data serving as basic data of a prediction algorithm, a prediction mode selection module used for selecting a proper prediction algorithm to predict the power of a wind power plant or a photovoltaic power plant, a prediction algorithm module used for predicting the power of the wind power plant or the photovoltaic power plant and comprising a physical model, an adaptive logic network algorithm model and a BP neural network model, and a prediction result storage module for storing result data predicted by the prediction algorithm module. According to the method, the numerical value weather forecast data is used as input data of the prediction algorithm, the most proper prediction method is selected through the mode selection module, and a power prediction result is obtained.

The invention discloses a dynamic combination analysis method of new energy generating capacity influenced by meteorological information in the field of application intersection of energy-saving economic dispatch of a power grid and computer artificial intelligence. The method comprises the following steps of: firstly, carrying out data pre-processing; secondly, dividing the actually-measured data of historic records or weather predictions into a plurality of sample sets according to different terrain heights, wherein each sample set provides initial weight distribution; thirdly, training thedifferent sample sets by using a particle swarm algorithm and a plurality of learning algorithms to generate a plurality of analysis models, wherein the particle swarm algorithm is used for automatically optimizing algorithm parameters, and each learning algorithm adjusts the weight distribution of samples in the corresponding sample set according to accuracy; fourthly, increasing weights so as to highlight large-error samples, otherwise, decreasing the weights; fifthly, adjusting the weights among the respective learning algorithms according to the calculation accuracy of each model, decreasing the weights of large-error models, otherwise, increasing the weights; and finally, forecasting according to a plurality of training models which are generated finally and the weight distribution among the plurality of training models.

The invention provides a power distribution network line fault risk day prediction method and system. The method comprises steps that the external environment information of the location area of a tested line on the prediction date, load prediction data of the tested line on the prediction date, an operation plan of the tested line on the prediction date and the self status information of the tested line on the prediction date are acquired; the acquired external environment information of the tested line on the prediction date, the acquired load prediction data of the tested line on the prediction date, the acquired operation plan of the tested line on the prediction date and the acquired self status information of the tested line on the prediction date are inputted to a pre-constructed line fault risk day prediction model, and a fault generation probability prediction value of the tested line on the prediction date is generated. The method is advantaged in that based on line load prediction, weather prediction, the operation plan, the line operation environment and other situations, line fault risk day prediction is carried out, so relevant fault avoidance measures are adopted, fault generation is avoided, and power supply reliability is guaranteed.

The invention belongs to the technical field of a power system and particularly relates to an integrated platform system for remote management and control of a wind power field cluster for cross-regional multiple-wind-field unified management and control. The integrated platform system for the remote management and control of the wind power field cluster comprises a remote wind power field monitoring subsystem, a wind power prediction sub system, a video image monitoring subsystem and a large screen projection display subsystem, wherein subsystems are in communication connection with another other through a communication network; the remote wind power field monitoring subsystem is used for acquiring the operation data of wind power field booster station monitoring, box transformer substation monitoring and real-time fan monitoring; the wind power prediction subsystem is used for downloading numerical weather prediction information, receiving the data of a wind power field anemometer tower, performing wind power field output prediction on each wind power field in an ultrashort period of future 0-4 hours and short period of 0-72 hours; and the wind power prediction subsystem is further used for giving early warning on disaster weather. The integrated platform system for the remote management and control of the wind power field cluster is capable of realizing cross-regional, multi-wind field state monitoring and operation management and realizing the wind power prediction, state detection and fault treatment of the full-digital wind power fields.

A method, system, and device provide alert information using weather prediction data. The method includes: converting weather prediction data to a tile based tile set, the tiles each representative of a unique geographic projection of a rendered geographical area of defined size; determining for each time period of the forecast whether any of the one or more weather variables associated with each prediction geographical tile violates a weather variable threshold for the location associated with the prediction geographical tile, the determining performed by comparing for each time period of the forecast each weather variable of the one or more weather variables associated with the prediction geographical tile to the weather variable threshold for the location associated with the prediction geographical tile; and generating and communicating one or more alerts corresponding to one or more violations of the weather variable thresholds for the locations associated with the prediction geographical tiles.

The invention discloses an information entropy-based self-adaptive integrated classification method of data streams. Concept drift can be detected, and duplicate concepts can also be identified. In asystem, a new classifier is reconstructed and put into a classifier pool only when existence of a new concept is detected, the problem of duplicate training caused by duplicate concept appearance is prevented, model updating frequency is reduced, and real-time classification ability and classification effect of a model are improved. Through carrying out performance analysis comparison with classical data stream algorithms on a synthetic dataset and a real dataset, experiments show that the method of the invention can cope with multiple types of concept drift, improves anti-noise ability of theclassification model, and also has lower time cost consumption on the premise of ensuring higher classification accuracy. The method of the invention can be applied to many practical problems of sensor network anomaly detection, credit-card fraud behavior detection, weather forecasting, electricity price prediction and the like.

The invention discloses a deep-structure recurrent neural network-based PM2.5 prediction method. According to the method, construction a deep-structure PM2.5 prediction model is constructed accordingto deep learning and a recurrent neural network theory by utilizing acquired mass data; and through data feature extraction and training, hazy weather prediction is realized. The method aims at improving the haze prediction efficiency and precision and providing convictive decision basis for haze prevention and treatment. The prediction model does not requirements for a data structure and can carry out self-learning when data is big enough, so that deep learning is suitable for the requirements of the present internet big data applications.

The invention provides a wind power plant cluster short-term power prediction method based on a space-time diagram convolutional neural network, and the method comprises the steps: collecting historical power in a first target time period to obtain a historical power vector time sequence, and collecting weather forecast parameter vectors in a second target time period to obtain a weather forecastparameter matrix time sequence; inputting the historical power vector and the time sequence of the weather forecast parameter matrix into a prediction model, and outputting a predicted power vector time sequence in a third target time period, wherein the prediction model is obtained by training based on a sample historical power vector, a sample weather forecast parameter matrix timing sequence and a prediction power vector timing sequence label, and a neural network structure of the prediction model is formed based on a Bi-GRU network and a graph convolution network. According to the method provided by the invention, two factors of historical power and weather forecast parameters can be jointly considered in power prediction, and the prediction accuracy is also improved.

The present document describes a method for generating and displaying a succession of short-term weather forecasts, also called nowcasts, in selectable time increments. A system for preparing nowcasts, called nowcaster, is used for preparing short-term forecasted weather values with a default time increment between each one of them. The method receives a chosen time increment from a user and the prepared forecasted weather values. The method comprises an aggregator that re-packages the forecasted weather values in the chosen time increments. A succession of short-term weather forecasts, which is a collection of forecasted weather values at the chosen time increment, is then outputted.

The invention discloses a wind power climbing event probability prediction method and system based on a Bayesian network, and the method comprises the steps: mining the dependency relationship betweena wind power climbing event and related meteorological influence factors such as wind speed, wind direction, temperature, air pressure, humidity, and the like, and building a Bayesian network topological structure with the highest fitting degree with sample data; quantitatively describing a conditional dependency relationship between the climbing event and each meteorological factor, estimating the value of each conditional probability in a conditional probability table at each node of the Bayesian network, and forming a Bayesian network model for predicting the wind power climbing event together with a Bayesian network topological structure; deducing the probability of occurrence of each state of the climbing event according to the numerical weather forecast information of the mastered prediction time; the value of the corresponding conditional probability at each node is adaptively adjusted, so that the inferred conditional probability result of each state of the climbing event is optimized, and the compromise between the reliability and the sensitivity of the prediction result is realized.

The invention discloses a weather prediction method, which includes the steps: providing temperature information values, normalizing the temperature information values, establishing an input / output matrix of a training sample, predicting weather through a neural network based on the output matrix, and the like. Original training data modes can be automatically distinguished by an improved algorithm, and are subjected to sample establishing and normalization. The weather prediction method is applicable to various complex circumstances, is high in flexibility, and needs no auxiliary data to complete prediction, and prediction results can be restored within a numeric range corresponding to original training data.

A system and method for managing all of the HVAC components of a building so they operate like one optimized ecosystem. The system and method optimize HVAC operations by predicting HVAC requirements based historical data, weather forecasts and / or occupancy rates. Historical data includes data obtained from an observation phase in which sensor data is collected from the building and used to train the algorithms utilized in the system and method and thereby providing for a more accurate prediction of HVAC requirements. In some embodiments, the systems and methods are configured to predict future required operational parameters using an artificial intelligence engine trained using historical data from the HVAC components.

A computerized method of computing the Voronoi diagram has applications including communications networks, robotics, three-dimensional networks, materials science, searching image processing, data clustering, data compression, control of a groups of methods for image processing and the like, design of electronic circuits, geographic information systems, solutions of the efficient location problem, face recognition, mesh generation and re-meshing, curve and surface generation / reconstruction, solid modeling, collision detection, controlling motion of vehicles, navigation, accident prevention, data clustering and data processing, proximity operations, nearest neighbor search, numerical simulations, weather prediction, analyzing and modeling proteins and other biological structures, designing drugs, finding shortest paths, pattern recognition and as an artistic tool. The Voronoi diagram is a decomposed region X made into cells, the decomposition being induced by a set of generators (Pk)k-K, and a distance function, and involves finding for each generator Pk a cell, which is a set of all the points in X satisfying the condition that the distance to the current generator P=Pk is not greater than the distance thereof to the union A of the other generators, The method comprising: for each generator, and for each point p in this generator, selecting a set of directions, then for each direction recursively testing a ray in that direction, until a certain interval on the ray is of length less than or equal to a given error parameter. A point corresponding to the interval on the ray is then selected as an end point, the cells are defined from the end points, thus forming the Voronoi diagram.