Abstract: Attribute information of a pending data handling process to be performed is identified. Historical data handling processes for a same application scenario as the pending data handling process are identified based on the attribute information. A time that each identified historical data handling process is performed and methods used to perform the identified historical data handling processes are also identified. A weighting coefficient for each identified historical data handling process is determined. Weighted identified historical data handling processes are calculated by applying the weight coefficient to each identified historical data handling process. A recommended weight for each method used to perform the identified historical data handling processes is determined by combining weighted identified historical data handling processes that use the same method. A method that has the highest recommended weight to perform the pending data handling process is recommended.

Abstract: Attribute information of a pending data handling process to be performed is identified. Historical data handling processes for a same application scenario as the pending data handling process are identified based on the attribute information. A time that each identified historical data handling process is performed and methods used to perform the identified historical data handling processes are also identified. A weighting coefficient for each identified historical data handling process is determined. Weighted identified historical data handling processes are calculated by applying the weight coefficient to each identified historical data handling process. A recommended weight for each method used to perform the identified historical data handling processes is determined by combining weighted identified historical data handling processes that use the same method. A method that has the highest recommended weight to perform the pending data handling process is recommended.

Abstract: A method and device for modeling a long-time-scale photovoltaic output time sequence are provided. The method includes that: historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected (101); weather types of days corresponding to the photovoltaic output are acquired from a weather station (102), and probabilities of transfer between each type of weather are calculated respectively (103); and a simulated time sequence of the photovoltaic output within a preset time scale is generated (104), and its validity is verified (105). By the method, annual and monthly photovoltaic output simulated time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be acquired according to different requirements to provide a favorable condition and a data support for analog simulation of time sequence production including massive new energy.

Abstract: A method and device for modeling a long-time-scale photovoltaic output time sequence are provided. The method includes that: historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected (101); weather types of days corresponding to the photovoltaic output are acquired from a weather station (102), and probabilities of transfer between each type of weather are calculated respectively (103); and a simulated time sequence of the photovoltaic output within a preset time scale is generated (104), and its validity is verified (105). By the method, annual and monthly photovoltaic output simulated time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be acquired according to different requirements to provide a favorable condition and a data support for analogue simulation of time sequence production including massive new energy.

Abstract: A method and device for modeling a long-time-scale photovoltaic output time sequence are provided. The method includes that: historical data of a photovoltaic power station is acquired, and a photovoltaic output with a time length of one year and a time resolution of 15 mins is selected (101); weather types of days corresponding to the photovoltaic output are acquired from a weather station (102), and probabilities of transfer between each type of weather are calculated respectively (103); and a simulated time sequence of the photovoltaic output within a preset time scale is generated (104), and its validity is verified (105). By the method, annual and monthly photovoltaic output simulated time sequences consistent with a random fluctuation rule of a photovoltaic time sequence may be acquired according to different requirements to provide a favorable condition and a data support for analogue simulation of time sequence production including massive new energy.

Abstract: Attribute information of a pending data handling process to be performed is identified. Historical data handling processes for a same application scenario as the pending data handling process are identified based on the attribute information. A time that each identified historical data handling process is performed and methods used to perform the identified historical data handling processes are also identified. A weighting coefficient for each identified historical data handling process is determined. Weighted identified historical data handling processes are calculated by applying the weight coefficient to each identified historical data handling process. A recommended weight for each method used to perform the identified historical data handling processes is determined by combining weighted identified historical data handling processes that use the same method. A method that has the highest recommended weight to perform the pending data handling process is recommended.