Wind speed clustering using linkage-ward method: A case study of Khaaf, Iran

Abstract

The use of renewable energy for providing electricity is growing rapidly. Among others, wind power is one of the most appealing energy sources. The wind speed has direct impact on the generated wind power and this causes the necessity of wind speed forecasting. For better power system planning and operation, we need to forecast the available wind power. Wind power is volatile and intermittent over the year. For getting better insight and a tractable optimization problem for different decision making problems in presence of wind power generation, it is required to cluster the possible wind power generation scenarios. This article presents probabilistic wind speed clustering prototype for wind speed data of Khaaf, Iran. This region is known as one of the high potential wind sites in Iran and several wind farm projects is planned in this area. The average speed of wind for a ten-minute period measured at height of 40m over a year (2008) is used for clustering. From the result of this research, the most appropriate probabilistic model for the wind speed can be obtained.