Statistical Processing of Wind Speed Data for Energy Forecast and Planning

Abstract

This paper presents a statistical approach to

manage wind speed sampled data in order to obtain the

forecast of the wind energy potential of a given site. The

proposed statistical method is the k-means clustering that

allows to extract from a set of experimental measurements

the sub-sets of useful data for describing the energy

capability of the site. The wind speed distributions in

different sites in Sicily, in the south of Italy, have been

studied as case study. A suitable wind generator, matching

the wind profile of the studied sites, has been selected for

the evaluation of the producible energy. It is demonstrated

that the use of the proposed method simplifies the problem

of the wind plant energy assessment respect to the option of

obtaining the desired information by managing a large

amount of experimental observations. The proposed method

represents a useful tool for an appropriate energy planning

in distributed generation systems.