This paper provides a case study on application of wavelet techniques to analyze wind speed and energy (renewable and environmental friendly energy). Solar and wind are main sources of energy that allows farmers to have the potential for transferring kinetic energy captured by the wind mill for pumping water, drying crops, heating systems of green houses, rural electrification’s or cooking. Larger wind turbines (over 1 MW) can pump enough water for small–scale irrigation. This study tried to initiate data gathering process for wavelet analyses, different scale effects and their role on wind speed and direction variations. The wind data gathering system is mounted at latitudes: 37º 50″ N; longitude 30º 33″ E and height: 1200 m above mean sea level at a hill near Süleyman Demirel University campus. 10 minutes average values of two levels wind speed and direction (10m and 30m above ground level) have been recorded by a data logger between July 2001 and February 2002. Wind speed values changed between the range of 0 m/s and 54 m/s. Annual mean speed value is 4.5 m/s at 10 m ground level. Prevalent wind direction is south west. Artificial Neural Network analyses are also applied to predict wind speed values in winter and spring.