671 results about "Weather prediction" patented technology

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.

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 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.

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 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.

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 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.

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 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 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 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 (P_k)_k-K, and a distance function, and involves finding for each generator P_k a cell, which is a set of all the points in X satisfying the condition that the distance to the current generator P=P_k 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.

The invention discloses a rain pump station monitoring system. The rain pump station monitoring system comprises a collecting tank, an interception pump, a rain pump, a monitor, a pipe network hydraulic model module, and a weather prediction model module; the interception pump and the rain pump are provided with stroke control devices; the monitors is arranged in the pipe of the interception pump and the rain pump in the collecting tank; the pipe network hydraulic model module and the weather prediction model module are connected; and the monitor is connected with the interception pump and the rain pump which are provided with the stroke control devices through the pipe network hydraulic model module, and the weather prediction model module. The running of the interception pump and the rain pump is monitored on line in real time through the monitor, the weather prediction model module and the hydraulic model module, the discharge of mixed water of rain and sewage and rain in the collecting tank is effectively controlled, the pollution of the discharge of the mixed water of rain and sewage to the environment is effectively controlled, the rain accumulation function of the rain pump station is fully conducted and surface gathered water is prevented from being formed.

The invention relates to the technical field of renewable energy source recovery and utilization, in particular to a multi-user region energy source Internet system and a regulating method thereof. A region energy source network is built on the basis of an electric network, a heat network and a cold network; the multi-user region energy source network self regulation is realized through the cooperated complementation of the region energy source network and an out-of-region urban energy network, the cooperated complementation of region energy source networks and the cooperated complementation of the region energy network per se and the energy storage. Through a weather prediction model, a multi-user energy source demand prediction model, a renewable energy source prediction model and a region energy source network optimization module, the operation optimization of the region energy source network is realized.

An integrated multi-scale modeling platform is utilized to assess agricultural productivity and sustainability. The model is used to assess the environmental impacts of agricultural management from individual fields to watershed/basin to continental scales. In addition, an integrated irrigation system is developed using data and a machine-learning model that includes weather forecast and soil moisture simulation to determine an irrigation amount for farmers. Next, crop cover classification prediction can be established for an ongoing growing system using a machine learning or statistical model to predict the planted crop type in an area. Finally, a method of predicting key phenology dates of crops for individual field parcels, farms, or parts of a field parcel, in a growing season, can be established.

The invention relates to a ventilation system and control method of sunlight greenhouse rolling blinds based on intelligent prediction. The system comprises a motion execution mechanism, a controller and a data processing decision module; the motion execution mechanism comprises a rolling blind motor, a film rolling motor, a motor rotating shaft, a telescopic rod and a spacing protecting device; the controller comprises a single-chip microcomputer, an environment sensor, a memory card, a wifi module, an angle sensor and a motor control circuit; the data processing decision module comprises a historical data query system, a weather forecast and query system, real-time meteorology data acquisition system and a collaborative optimization processing system. The data processing decision module of the invention generates the scheme of controlling the rolling blinds and ventilating; the controller sends instructions of start-up or shut-down of the motor to complete rolling blinds and rolling films through the motor control circuit. The system and method of the invention increase the accuracy of spacing protection stroke: piecewise function control is adopted so as to avoid rapid environment temperature change affecting the growth of crops when bottom ventilating openings are opened due to large temperature difference.

The invention discloses a seasonal precipitation analogue prediction method based on a seasonal prediction mode, and relates to the technical field of weather prediction of meteorology. The method comprises the following steps of according to historical seasonal mode prediction information, obtaining a mode prediction error principal component and feature vectors corresponding to the mode prediction error principal component; judging whether the mode prediction error principal component can be predicted or not; when the mode prediction error principal component can be predicted, performing the analogue prediction of a time coefficient; when the mode prediction error principal component cannot be predicted, evaluating the time coefficient, and combining the analogue prediction result and evaluation result of the time coefficient, and the feature vectors corresponding to the mode prediction error principal component, so as to form mode prediction errors; utilizing the mode prediction errors to correct seasonal precipitation mode prediction results. By adopting the technical scheme, the method has the advantages that the evolution information of prediction factors can be fully and effectively utilized, the time scale feature of the mode prediction is fully considered, and the working efficiency of prediction persons and the accuracy of prediction results are greatly improved.