190 results about "Weather parameter" patented technology

A method for providing location-based nowcast, comprises the steps of: a) initiation of a request, by an end-user, for a time evolution of user-selected nowcast weather parameters, the transmission of the request being made over a public communication network to a nowcast request processor; b) the determination of the end-user's actual location by the nowcast request processor; c) the nowcast request processor gathering raw weather data from data sources; d) processing the raw data into a time-series of nowcast parameter maps; e) extracting the time evolution of the user requested nowcast parameters from the time-series of nowcast parameter maps by positioning the end-user's location on said time series; and f) distributing the requested information to end-user regarding the user-selected nowcast parameters within the extracted time evolution.

The invention relates to an energy-saving optimized control system and an energy-saving optimized control method for a refrigerator room. The energy-saving optimized control system comprises an industrial control computer, a flow sensor, a temperature sensor, an outdoor temperature and humidity sensor, a three-phase active power transmitter, a water pump frequency converter and a cooling tower fan frequency converter which are connected with a programmable logic controller respectively, wherein the programmable logic controller communicates with the industrial control computer through an industrial Ethernet; and an RS485 communication interface module is connected with a water cooling unit and communicates with the industrial control computer through a ModBus protocol. A mathematical model of relationships between the energy consumption and running parameters of each equipment in the refrigerator room is established to regulate the running state of each equipment in combination with real-time refrigeration loads and weather parameters, thereby fulfilling the aim of making the lowest running energy consumption of the whole refrigerator room at the premise of meeting the refrigeration loads.

A method and system for performing quality control checks upon weather data are provided. The weather data includes information pertaining to a plurality of weather parameters. The quality control checks may identify inaccurate weather parameters or be used to rank weather data sets. The quality control checks include hourly, daily, and forecast quality control checks performed upon hourly, daily, and forecast weather parameter data. The method and system may exclude the inaccurate weather parameters identified from a reasonable weather database. The method and system may determine reasonable or approximate weather parameters that approximate the actual weather parameters. The system may rank a plurality of weather data sets based upon the accuracy determination. The reasonable weather data or the weather data ranked as the most accurate may be used to calculate energy or cost savings for building energy conservation measures.

The invention discloses a photovoltaic power generation system power predicting method of an elman-based neural network. The method comprises the steps of obtaining generated power historical data and corresponding historical weather parameter information of photovoltaic power generation equipment at the related area, determining input data and output data of the neural network, determining the optimal number of hidden layer neurons, building the elman-based neural network accordingly, carrying out normalization processing on the generated power historical data and the historical weather parameter information, training the built neural network according to the data obtained after normalization processing is carried out, controlling prediction errors of the elman-based neural network to be within the preset range accordingly, regarding generated power historical data of one week before the prediction day and weather parameter data of the prediction day as input, and predicting the generated power of the prediction day through the trained neural network. The method has the advantages of being stable and high in time-varying adaptability and prediction precision, and can be widely applied to the field of photovoltaic power generation.

A method of method of calculating an ET value for an irrigation area is disclosed. The method includes a weather parameter measurement system collecting at least one weather parameter of a weather parameter area, the at least one weather parameter of the weather parameter area providing a representation of weather outside of the irrigation area. An irrigation area weather parameter is calculated providing a representation of the weather within the irrigation area from the at least one weather parameter of the weather parameter area. The ET value for the irrigation area is calculated from an N-dimensional continuous function using the at least one irrigation area weather parameter, wherein the N-dimensions includes at least a 3-dimensional spatial cube.

A method of method of calculating an ET value for an irrigation area is disclosed. The method includes a weather parameter measurement system collecting at least one weather parameter of a weather parameter area, the at least one weather parameter of the weather parameter area providing a representation of weather outside of the irrigation area. An irrigation area weather parameter is calculated providing a representation of the weather within the irrigation area from the at least one weather parameter of the weather parameter area. The ET value for the irrigation area is calculated from an N-dimensional continuous function using the at least one irrigation area weather parameter, wherein the N-dimensions includes at least a 3-dimensional spatial cube.

The invention discloses a residential load prediction method of an elman-based neural network. The method comprises the following steps: acquiring residential load historical data of last year and corresponding historical weather parameter data; calculating a seasonal index of the residential load of each month; correcting the residential load historical data by using the seasonal index; determining input and output data of a neural network and determining an optimal hidden layer neuron number so as to establish the elman-based neural network; normalizing the corrected residential load historical data and the corresponding historical weather parameter data to further train the established neural network and control the prediction error in a preset range; predicting the residential load by using the trained neural network. The method has the capacity of adapting to time-variant characteristic and seasonal fluctuation of the residential load, the dynamic characteristic of the residential load can be directly predicted and reflected, the prediction accuracy is high, and the method can be widely applied to the charge prediction field of a power system.

Systems and methods for determining a power disruption index indicative that provides subscribers with a forecast of weather conditions that are likely to cause interruptions to power distributions systems within their specific areas of service. The Power Disruption Index (PDI), is a calculation of a number of forecast weather parameters including severe thunderstorm probabilities and intensities, wind speeds, wind gusts, and snowfall and ice accretion. The index combines each of these input parameters with a specific weighting based on the forecast intensity of each of the parameters, along with alert threshold criteria provided by each client utility. The output PDI is a forecast of local weather conditions for a specific local service area or power distribution network.

Estimating energy generated by a solar system in a predetermined geographic area comprises, at each predetermined time instant: retrieving measured values of at least one weather parameter and of solar irradiance in the geographic area, the values related to a time slot before the predetermined time instant; performing auto-regression analysis of the measured values; estimating, based on the auto-regression analysis, a relationship between the at least one weather parameter and the solar irradiance; retrieving forecasted values of the at least one weather parameter in the geographic area, the forecasted values being forecasted for a second time slot after the predetermined time instant; performing regression analysis of the relationship between the at least one weather parameter and the solar irradiance of the forecasted values; forecasting solar irradiance in the second time slot based on the regression analysis, and estimating energy generated by the solar system in the second time slot.

A method for short term load forecasting in a power grid includes obtaining historical data comprising power data, load data and weather data corresponding to time index data recorded from a location in a power distribution network of the power grid. The method further includes receiving power grid data comprising a plurality of power values, and a plurality of weather parameter values corresponding to a plurality of recent time instant values. The method also includes generating modified historical data using statistical techniques to rectify error conditions. The method further includes estimating one or more power values at a future time instant based on the modified historical data and the power grid data. The method also includes balancing load of the power distribution network based on the estimated one or more power values.

The invention provides a method and device for testing air-conditioning filter screen cleanliness, electronic equipment and a storage medium. The method comprises the following steps of obtaining operation parameters of the current cycle of air-conditioning equipment and local weather parameters; obtaining the filter screen usage information of the current cycle of the air-conditioning equipment according to the operating parameters of the current cycle of the air-conditioning equipment and the local weather parameters; generating the filter screen cleaning prompt information of the current cycle of the air-conditioning equipment according to the filter screen usage information of the current cycle of the air-conditioning equipment and the filter screen usage information of each historicalcycle in the cumulative period when a filter screen is not cleaned; and sending the filter screen cleaning prompt information to preset target equipment. The technical problem that an air-conditioning filter screen cleanliness in the prior art has high cost and low accuracy can be effectively solved, and the situation that the use condition of the air-conditioning filter screen is determined according to the actual use of the air-conditioning equipment and air condition is achieved, so that the accuracy of the air-conditioning filter screen cleanliness testing is improved while the cost is reduced.

The invention discloses a central heating system on-demand precise regulation and control method based on models. The method comprises the following steps that S1, a heating station load predicting model is built according to the historical operation data of a heating station and weather data; S2, the next-day short-term heat load of the heating station is predicted under the given weather condition; S3, needed secondary side heat supply and flow are determined according to the needed heat load; S4, a plate exchange model of a heating station heat exchanger is built; S5, the needed primary side valve opening degree is calculated through the plate exchange model according to the needed heat load and secondary side heat supply temperature, and related control operation is executed accordingly; S6, a data driving model of the relation among the room temperature, return water temperature and weather parameters of all buildings is built; and S7, the set value of the return water temperatureof all building openings is calculated according to the models, so that adjusting valves of the building openings track the value. According to the method, the situation of excessive supply of the heating station can be effectively avoided, heat energy loss can be reduced, and the utilization efficiency of the heating station on energy is improved.

The invention discloses an air conditioner and an adjusting method and device for an air conditioner running strategy. The method comprises the steps that historical running data and historical weather parameters of the air conditioner are acquired, wherein the historical running data correspond to a working strategy of the air conditioner; the working strategy of the air conditioner is adjusted to a target running strategy according to the historical running data; after current weather parameters are detected, weather difference values between the current weather parameters and the historicalweather parameters are determined; and if it is determined that the weather difference values between the current weather parameters and the historical weather parameters are within a preset weatherrange, the current working strategy of the air conditioner is adjusted to the target running strategy. According to the air conditioner and the adjusting method and device for the air conditioner running strategy, the technical problems that in the related technologies, the air conditioner using comfortableness is poor for a user and energy consumption is high due to the fact that a control strategy of the air conditioner can not be matched with the actual environment, and output abnormity and frequent starting and stopping are prone to occurring are solved.

A method and system for performing quality control checks upon weather data are provided. The weather data includes information pertaining to a plurality of weather parameters. The quality control checks may identify inaccurate weather parameters or be used to rank weather data sets. The quality control checks include hourly, daily, and forecast quality control checks performed upon hourly, daily, and forecast weather parameter data. The method and system may exclude the inaccurate weather parameters identified from a reasonable weather database. The method and system may determine reasonable or approximate weather parameters that approximate the actual weather parameters. The system may rank a plurality of weather data sets based upon the accuracy determination. The reasonable weather data or the weather data ranked as the most accurate may be used to calculate energy or cost savings for building energy conservation measures.

Disclosed are methods, systems, and non-transitory computer-readable medium for managing energy use in a vehicle. For instance, the method may include receiving forecasted data from a first external source, receiving real-time data corresponding to at least one weather parameter at a first location at a first time, and continuously determining whether to perform an adjustment to a control parameter of the vehicle by using a machine learning model that is based on the forecasted data for the at least one weather parameter, the real-time data for the at least one weather parameter, a battery condition of the vehicle, and / or an estimated amount of energy consumed by traveling along a first navigation path.

The invention discloses a method for automatic resource setting of a mobile terminal according to weather. The method comprises the steps of firstly prestoring at least a set of resources according to weather parameters, secondly obtaining a current geographical position of a person who receives a call, thirdly obtaining weather information of the geographical position within a preset time period, and fourthly choosing a set of resources from the at least one set of resources according to the weather information to display or play as the current resource. According to the method, weather conditions of the current position where the person who receives the call is located can be automatically linked together with resources of the local mobile terminal, a user can be informed of the weather conditions of the current position where the person who receives the call is located through voice or desktop effect modes, and novel utilization experience can be provided for the user.

The invention provides an intelligent bright illumination control system based on a weather condition. The intelligent bright illumination control system comprises a sensing terminal, a communication gateway and a monitoring center, wherein the sensing terminal is used for monitoring weather parameter data information; the communication gateway communicates with the sensing terminal and acquires the weather parameter data information, and the monitoring center is used for receiving the weather parameter data information sent by the communication gateway; and the sensing terminal comprises a temperature sensor, a humidity sensor and a brightness sensor. The intelligent bright illumination control system has the advantages that: light control is realized by detecting the weather condition, so energy conservation and emission reduction are realized; and the cost is low, the volume is small, and the intelligent bright illumination control system is convenient to install and maintain.

The invention is applicable to the field of weather parameter measurement and provides a radiosonde and a weather data measuring method thereof. The method includes: using a temperature sensor, a humidity sensor and a barometric sensor installed on the radiosonde to measure atmospheric temperature, humidity and pressure parameters; using an accelerometer to measure acceleration of the radiosonde; using a navigation satellite signal receiving module to receive a navigation satellite signal; subjecting measurement data to data encoding and modulating, and using a radio transmitter to transmit the data to a ground receiving device; using the ground receiving device to calculate current atmospheric temperature, humidity, pressure, wind speed and wind direction parameters according to the received data. The radiosonde and the weather data measuring method thereof have the advantages that the wind speed and wind direction can be calculated according to measurements of the accelerometer even when the navigation satellite signal receiving module of the radiosonde loses satellite signals.

The invention belongs to the power transmission line risk early warning assessment technical field and relates to a Poisson distribution-based power transmission line tripping risk assessment method. According to the method, a regional power grid is divided into a plurality of meteorological regions according to historical meteorological data; ledger information, meteorological information, tripping information and the like of a power transmission line are organized according to the regions; a power transmission line fault rate distribution formula is established based on a Poisson distribution equation; meteorological parameters in the meteorological regions are segmented; distribution parameters and meteorological data of meteorological blocks are put into statistics; and a regression analysis method is adopted to establish a power transmission line tripping probability distribution model of wind and rain weather parameters, wherein the power transmission line tripping probability distribution model is used for assessing tripping risks under heavy rain and strong wind environments. According to the Poisson distribution-based power transmission line tripping risk assessment method of the invention, the tripping rate of the power transmission line is analyzed, and with the increase of statistical data, the accuracy of the method is continuously increased; and Poisson distribution is adopted to perform risk prediction, and therefore, the method is more suitable for random event prediction of an approaching time period.

The invention discloses a building electrical load comprehensive prediction method and system based on an LSTM neural network. The method comprises the steps of obtaining load data, weather parametersand building data of a typical building and performing normalization processing; establishing an electrical load prediction model of the LSTM neural network, selecting data of similar typical days astraining samples, wherein the training data comprises weather factors of the training days, building type data and load data, and training is carried out with the minimum electrical load error as thetarget in the training process to obtain LSTM neural network model parameters; and inputting building data of a to-be-tested building to the trained electrical load prediction model of the LSTM neural network, and then obtaining a typical daily load curve, a monthly load curve and an annual load curve corresponding to the building. According to the building load prediction method based on the LSTM neural network, different characteristics and load fluctuation change conditions of a building are comprehensively considered to realize high-precision load prediction of the building, and the method has the functions of high precision and easy realization.

The invention relates to an air conditioner control method and device, an air conditioner and an air conditioning system. The method comprises the steps that identity label information sent by a mobile terminal is received, and current weather parameters are acquired; a corresponding user database is read according to the identity label information, wherein weather data and air conditioner operation parameters are stored in the user database, and the weather data and the air conditioner operation parameters have a preset binding relationship; the weather data matched with the weather parameters and the air conditioner operation parameters corresponding to the weather data are acquired from the user database; and air conditioner control is carried out according to the acquired air conditioner operation parameters. After a user is recognized by the received identity label information sent by the mobile terminal, air conditioner parameters are set for the user according to the air conditioner using habit of the user in the current environment, the air conditioner can operate according to the most suitable scheme, the most comfortable environment is provided for the user, user use steps are simplified, and user use is convenient.

Embodiments of the invention disclose a weather data-based production early warning method and apparatus, which is used for realizing automatic early warning of plant production based on weather datato improve the timeliness and validity of early warning measure implementation. The method comprises the steps of obtaining weather forecast data of a production base in the internet of things througha preset data interface; extracting weather parameters corresponding to the production base from the weather forecast data; converting the weather parameters into corresponding weather symbol images;determining plant varieties of the production base corresponding to the weather symbol images; and according to the plant varieties, converting the weather symbol images into corresponding action execution instructions through an action mapping table, wherein the action execution instructions are used for performing early warning prompting on plants of the production base.

The application relates to an air conditioner heating control method, a controller and an air conditioner, and belongs to the air conditioner heating technology field. The air conditioner heating control method includes: acquiring weather parameters, wherein the weather parameters comprise temperature and humidity; confirming if normal heating running is needed or defrosting heating running is needed according to the weather parameters; and performing heating running control according to a confirmed result, wherein under the normal heating running, all coolant output by a compressor flows towards an indoor heat exchanger, and under the defrosting heating running, a part of the coolant flows into the indoor heat exchanger, the other part of the coolant flows towards a part, confirmed to perform defrosting, of heat exchange units in an outdoor heat exchanger, and the other heat exchange units in the outdoor heat exchanger are in a normal work state. The air conditioner heating control method can achieve adaptive continuous heating control by using the weather parameters, facilitates grasp of switching time of the normal heating running and the defrosting heating running well, and then facilitates improvement of heating effect and improvement of comfort experience of users.

The invention provides a short-term wind power prediction method and system based on a deep learning network. The short-term wind power prediction method comprises the steps of obtaining numerical weather forecast data of an area where to-be-predicted wind power is located; inputting the numerical weather forecast data into a pre-trained deep learning mapping model to obtain a predicted value of the wind power, wherein the deep learning mapping model comprises a corresponding relationship between the numerical weather forecast data and the wind power prediction value; and forming a grid by thenumerical weather forecast data according to positions, wherein each grid point in the grid comprises a plurality of weather parameters. According to the invention, the short-term power prediction precision of the wind power plant can be improved, meanwhile, the modeling time of the regional wind power prediction model can be shortened, and required computing resources and manpower resources arereduced.

The invention provides a control method and system of a washing machine and the washing machine. The washing machine comprises a step of acquiring state parameters which comprise clothes parameters ofclothes to be washed and weather parameters of a place where the washing machine is located, and a step of generating a washing program according to the preset rule according to the state parameters.The problem that the user experience is low caused by a condition that a laundry program set by the user and a preset laundry program are not adapted to the weather parameters in the prior art is solved.

The invention discloses a k-VNN- and LS-SVM-based modelling method for the icing of an electric transmission line. The k-VNN- and LS-SVM-based modelling method comprises the following steps of: taking the historical data provided by the micro-weather station of an overhead line structure as basis, reading in micro-weather parameter values and converting the micro-weather parameter values to a vector form; introducing a k-VNN algorithm so as to select proper samples from a line icing sample database, and calculating the Euclidean distances and included angle information of information vectors; selectively deleting and reserving the similar areas, that is, adjacent points, of the information vectors to form a training sample; optimizing the quantity of the samples selected by the k-VNN adjacent algorithm, by a cross-validation method, so as to acquire proper parameters such as the width delta of a kernel function K(xi, xj) and an error penalty factor gamma in an LS-SVM model, and finding the optimal one; after the parameters are set, training related data by a least squares support vector machine (LS-SVM), and finally acquiring an icing thickness. The algorithm disclosed by the invention is high in prediction accuracy, extremely fast in speed, and suitable for short-term icing prediction for the icing of the electric transmission line.

The invention relates to a simulation test method and device, computer equipment and a storage medium. The method can be applied to a simulation test of an artificial intelligence algorithm, and further comprises the following steps: creating a simulation radar in a simulation environment, and setting a virtual target in the simulation environment; obtaining weather parameters corresponding to thesimulation weather state generated in the simulation environment; controlling the simulation radar to emit a radar signal at a set emission energy value in a simulation environment; after the radar signal detects the virtual target, obtaining an echo energy value corresponding to the radar signal in a simulated weather state based on the emission energy value and the weather parameter; and performing a simulation test according to the echo energy value. According to the technical scheme provided by the invention, the attenuation of the radar signal in the corresponding real weather state canbe effectively reflected, that is, the echo energy value is more suitable for the actual situation, the effectiveness of radar system simulation is improved, and the subsequent simulation test carriedout according to the echo energy value is more reliable.

The invention provides an intelligent control system for a fountain in a scenic spot. The intelligent control system utilizes a weather parameter which is acquired by a meteorological monitoring module for determining whether to start the fountain and furthermore acquires real-time position information of a passenger through a smart traveling terminal. The intelligent control system utilizes the real-time position information for determining whether to start the fountain and selects different viewing modes. The intelligent control system performs fountain driving control based on the selected viewing mode, thereby performing fountain show in the selected viewing mode.

Methods, apparatuses, systems, and computer program products for providing dangerous driving weather messages in a tile-based system are provided. An example method comprises receiving one or more measurements of one or more weather parameters for a geographic area. The geographic area is represented by a plurality of tiles. The method further comprises processing at least one of the measurements of at least one of the one or more weather parameters to determine a dangerous driving weather score for at least a first tile of the plurality of tiles; identifying a first tile of the plurality of tiles that is experiencing dangerous driving weather based on the dangerous driving weather score for the first tile; generating a dangerous driving weather message; and providing the dangerous driving weather message. The dangerous driving weather message comprises a timestamp, a list of one or more affected tile identifiers, an alert type, and alert information.