Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.

Abstract: A method and apparatus for forecasting output power of wind turbine in a wind farm. The present invention provides a method for forecasting output power of a wind turbine in a wind farm, including: generating a corrected data set based on environmental data collected from at least one sensor in the wind farm; correcting a weather forecasting model by using the corrected data set; obtaining a forecast value of wind information at the wind turbine based on the corrected weather forecasting model; and forecasting the output power of the wind turbine based on the forecast value and a power forecasting model.

Abstract: A method and apparatus for forecasting output power of wind turbine in a wind farm. The present invention provides a method for forecasting output power of a wind turbine in a wind farm, including: generating a corrected data set based on environmental data collected from at least one sensor in the wind farm; correcting a weather forecasting model by using the corrected data set; obtaining a forecast value of wind information at the wind turbine based on the corrected weather forecasting model; and forecasting the output power of the wind turbine based on the forecast value and a power forecasting model.

Abstract: An abnormal area is detected using an initial spatial weights matrix between pairs of air quality sensors in a plurality of air quality sensors distributed across a geographical area and air quality data for each air quality sensor. The spatial weights matrix utilizes a distance between pairs of air quality sensors and wind direction through the geographical area. The initial spatial weights matrix and air quality data are used to calculate a plurality of local moran's indexes, one for each air quality sensor. The plurality of local moran's indexes are used to divide the plurality of air quality sensors into four groups. The groups are classified as proper or improper, and the proper groups are identified as abnormal areas.

Abstract: Correction management techniques are provided. In one embodiment, the techniques involve determining, via a first machine learning model, a first and second data based on a respective first and second raw data obtained from a plurality of sensors, determining, based on a deviation between the first data and the second data, an inaccuracy of the first data, identifying an ambient situation corresponding to the first raw data and the second raw data, selecting, from historical raw data of the plurality of sensors, a group of raw data corresponding to the ambient situation, and correcting, via a second machine learning model, the first data based on the selected group of raw data.

Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: An abnormal area is detected using an initial spatial weights matrix between pairs of air quality sensors in a plurality of air quality sensors distributed across a geographical area and air quality data for each air quality sensor. The spatial weights matrix utilizes a distance between pairs of air quality sensors and wind direction through the geographical area. The initial spatial weights matrix and air quality data are used to calculate a plurality of local moran's indexes, one for each air quality sensor. The plurality of local moran's indexes are used to divide the plurality of air quality sensors into four groups. The groups are classified as proper or improper, and the proper groups are identified as abnormal areas.

Abstract: A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.

Abstract: Embodiments of the present invention relate to methods, systems, and computer program products for correction management. In a method, a deviation may be detected by one or more processors between a first data obtained from a sensor in a plurality of sensors and a second data obtained from other sensors in the plurality of sensors, the first data and the second data being obtained in an identical or similar ambient situation. The ambient situation where the first data and second data are obtained may be identified by one or more processors. A group of raw data that is monitored under the ambient situation may be selected by one or more processors from historical raw data monitored by the plurality of sensors. The first data may be corrected by one or more processors based on the selected group of raw data.

Abstract: In an embodiment of the present disclosure, a method for modeling prediction of inhalable particles concentration is disclosed. In the method, at least one dispersal event is identified, and at least one accumulation event is identified based on the identified at least one dispersal event. Then a dispersal prediction model is generated based on the identified at least one dispersal event. Then at least one accumulation level of inhalable particles concentration is obtained for the at least one accumulation event. A change prediction model for the accumulation level is generated. Then a plurality of accumulation prediction models is generated.

Abstract: A method for tracking and identifying a polluted air mass's transmission trajectory in real 3-D space. In one aspect, a polluted air mass's transmission path identification is based on a monitoring of PM2.5 concentration in cubic volumes of an air mass. The method computes a transmission path of polluted air that considers wind-pressure conversion, the displacement estimation with mass concentration, and planetary boundary layer (PBLP height constraint) for 3-D cubic grids. The resultant determination of a polluted air mass's transmission trajectory in real 3-D space generates more practical and reliable results for intensive knowledge of the transport pathways and potential pollution sources in real 3-D space.

Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: A method and apparatus for forecasting output power of wind turbine in a wind farm. The present invention provides a method for forecasting output power of a wind turbine in a wind farm, including: generating a corrected data set based on environmental data collected from at least one sensor in the wind farm; correcting a weather forecasting model by using the corrected data set; obtaining a forecast value of wind information at the wind turbine based on the corrected weather forecasting model; and forecasting the output power of the wind turbine based on the forecast value and a power forecasting model.

Abstract: A mechanism is provided for forecasting air pollution. One or more air-pollution monitoring stations correlated to a forecasting point from a plurality of air-pollution monitoring stations are identified. For the one or more air-pollution monitoring stations that correlate to the forecasting point, one or more patterns of the forecasting point, historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point are identified. Based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point, a pollution forecast is provided.

Abstract: The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Abstract: A method for modeling air pollution includes receiving a weather model for a particular geographic region. Satellite-observed pollution observation data over the geographic region is received. A physical dispersion model for pollution over the geographic region is generated using the received weather model. The received satellite-observed pollution observation data is interpolated to the generated physical model. The interpolated satellite-observed pollution observation data and the generated physical model are combined using weighted coefficients for both the interpolated satellite-observed pollution observation data and the generated physical model. The weighted coefficients are calculated in accordance with a relative error in both the physical dispersion model and the satellite-observed pollution observation data.

Abstract: A system, method and computer program product for tracking and identifying a polluted air mass's transmission trajectory in real 3-D space. In one aspect, a polluted air mass's transmission path identification is based on a monitoring of PM2.5 concentration in cubic volumes of an air mass. The method computes a transmission path of polluted air that considers wind-pressure conversion, the displacement estimation with mass concentration, and planetary boundary layer (PBLP height constraint) for 3-D cubic grids. The resultant determination of a polluted air mass's transmission trajectory in real 3-D space generates more practical and reliable results for intensive knowledge of the transport pathways and potential pollution sources in real 3-D space.