The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South Ch...The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South China Sea（SCS） with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days（longer for some point station） in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.展开更多
The exploitation of wind energy is rapidly evolving and is manifested in the ever-expanding global network of offshore wind energy farms.For the Small Island Developing States of the Caribbean Sea(CS),harnessing this ...The exploitation of wind energy is rapidly evolving and is manifested in the ever-expanding global network of offshore wind energy farms.For the Small Island Developing States of the Caribbean Sea(CS),harnessing this mature technology is an important first step in the transition away from fossil fuels.This paper uses buoy and satellite observations of surface wind speed in the CS to estimate wind energy resources over the 2009–201911-year period and initiates hour-ahead forecasting using the long short-term memory(LSTM)network.Observations of wind power density(WPD)at the 100-m height showed a mean of approximately 1000 W/m^(2) in the Colombia Basin,though this value decreases radially to 600–800 W/m^(2) in the central CS to a minimum of approximately 250 W/m^(2) at its borders in the Venezuela Basin.The Caribbean Low-Level Jet(CLLJ)is also responsible for the waxing and waning of surface wind speed and as such,resource stability,though stable as estimated through monthly and seasonal coefficients of variation,is naturally governed by CLLJ activity.Using a commercially available offshore wind turbine,wind energy generation at four locations in the CS is estimated.Electricity production is greatest and most stable in the central CS than at either its eastern or western borders.Wind speed forecasts are also found to be more accurate at this location,and though technology currently restricts offshore wind turbines to shallow water,outward migration to and colonization of deeper water is an attractive option for energy exploitation.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41230421 and 41605075)the National Basic Research Program of China(Grant No.2013CB430101)
文摘The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South China Sea（SCS） with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days（longer for some point station） in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.
文摘The exploitation of wind energy is rapidly evolving and is manifested in the ever-expanding global network of offshore wind energy farms.For the Small Island Developing States of the Caribbean Sea(CS),harnessing this mature technology is an important first step in the transition away from fossil fuels.This paper uses buoy and satellite observations of surface wind speed in the CS to estimate wind energy resources over the 2009–201911-year period and initiates hour-ahead forecasting using the long short-term memory(LSTM)network.Observations of wind power density(WPD)at the 100-m height showed a mean of approximately 1000 W/m^(2) in the Colombia Basin,though this value decreases radially to 600–800 W/m^(2) in the central CS to a minimum of approximately 250 W/m^(2) at its borders in the Venezuela Basin.The Caribbean Low-Level Jet(CLLJ)is also responsible for the waxing and waning of surface wind speed and as such,resource stability,though stable as estimated through monthly and seasonal coefficients of variation,is naturally governed by CLLJ activity.Using a commercially available offshore wind turbine,wind energy generation at four locations in the CS is estimated.Electricity production is greatest and most stable in the central CS than at either its eastern or western borders.Wind speed forecasts are also found to be more accurate at this location,and though technology currently restricts offshore wind turbines to shallow water,outward migration to and colonization of deeper water is an attractive option for energy exploitation.
