Quality Evaluation of Wind Energy Data with Complete Linkage Clustering - Sakon Klongboonjit; Tossapol Kiatcharoenpol; T Unchai; A Janyalertadum; A Hold; S Chingulpitak; S Wongwises; P Quan; T Leephakpreeda; S Klongboonjit; T Kaitcharoenpol; H Seifoddini; et al. (International journal of intelligent engineering and systems, 2022)
Although wind is an important free energy and most investors or farmers would like to invest in wind energy projects, they sometimes lack of the wind quality data in alternative areas for making decision. It should be definitely good to have some simple methods to classify the quality of wind energy for alternative areas. In this study, Complete Linkage method combining with the Euclidean distance calculation, which is really a simple method for users, is introduced to cluster wind energy quality of alternative areas. In a case of 13 alternative areas in the south of Thailand, the data of average wind velocity along 12 months from the secondary data source can be used to generate the initial distance matrix before continuously improving with Complete Linkage method. Finally, these 13 alternative areas are suitable clustered at C.D. = 5.11 into 3 groups of the low wind quality area with I.D. = 1.21, the medium wind quality area with I.D. = 1.41 and the high wind quality area with I.D. = 1.45.
Keywords: Technology and Security Systems; Power Systems and Technologies; Smart Grid and Power Systems