The aim of this research is to study the wind speed, wind direction and the temperature of 15 stations of Thailand at 5 levels from 16 November 2019 - 13 February 2020. WAsP application is used in this research to calculate the two parameters Weibull distribution namely K shape and C scale. Furthermore, maximum and minimum wind speed is recorded. Data from Nakhon Si Thammarat shows the maximum mean wind speed 5.02 m/s. and Nan shows the minimum mean wind speed 0.8 m/s. Additionally, the prominent wind direction of every station is observed as well. Main wind direction for Nakhon Si Thammarat is from (Southwest). These results facilitate for the further research on wind characteristic feasible for small wind farm by increasing the timeline of data recorded.

Over the past decades, Wind energy is one of the alternative energy or renewable sources, which has been harvested to produce electricity. Our research aims to study the wind potential of the areas in the Rayong provinces of Thailand. Data from meteorological stations were collected every 10 minutes for of 3 years (2017-2019), with a measuring tower at 10-meter height above ground level (AGL). The annual average wind speeds were investigated in Rayong (2.02 m/s) with Weibull Probability Distribution Function (PDF). The annual average power density in Rayong regions was 13 W/m2. In all locations, wind direction was detected mainly from Southwest (SSW) and the yearly maximum wind power capacity is 94.376 MWh. The capacity factor of 21.5 % was noticed. With relatively low wind speed was noticed in Rayong provinces of Thailand, a small wind turbine installed at 30 meters would be recommended as a cost-effective way to convert wind power to electricity.

This study focuses on the collection and observation of mean wind speed and power density of 15 stations in Thailand merged with the topographic map of the stations. The wind data was collected by installing anemometers at 10m, 15m, 20m,25m and 30m height at 15 selected stations around Thailand. Wind Analysis and Application Program (WAsP) is used to generate mean wind speed and power density. Meanwhile, roughness and surface elevation map are produced and merged with the data in WAsP. The results showed the highest wind speed in Songkhla station which was 3.16 to 12.15m/s and on the other hand data from Narathiwat showed the lowest mean wind ranging from 1.13 to 1.72m/s. Finally, Songkhla station power density ranges from 24-1372W/m2 and in Narathiwat station ranging from 2-5W/m2 in terms of power density. In Thailand, the landscape is diverse such as plateau, plains, coastal plains, land, mountains, mountain ranges and hills. Generally, the wind speeds and directions change due to landscape. For this season, to study wind resource, investigation on topography is vital.