TY - JOUR
T1 - Determining the Economical Wind Power Sites for the Needed Power Loads Accounting for Geographical Terrains
AU - Wang, Wen Li
AU - Tang, Mei Huei
N1 - Publisher Copyright:
© 2016 Author.
PY - 2016
Y1 - 2016
N2 - Green power has attracted the world's attention and the proliferation of wind farms serve this purpose. However, wire connections among power sources and loads can be very costly in addition to the investment on the equipment. The geographical terrains also affect the routing cost, because the connection between two coordinates are often impractical to be a straight Euclidean distance. The Earth is a globe shape with curvature and a terrain can have rugged floors or water surfaces that hinder the convenience of wiring. The objective of this paper is to take advantage of a developed heuristic and apply learning algorithms to determine the best wind power sites. The goal is to conserve wiring expense and accommodate power loads. Terrain knowledge is incorporated by utilizing the geographical databases. Experiments are conducted to demonstrate the approach and justify cost savings. It is expected that this paradigm can be expanded to address more factors and support other green energy application domains.
AB - Green power has attracted the world's attention and the proliferation of wind farms serve this purpose. However, wire connections among power sources and loads can be very costly in addition to the investment on the equipment. The geographical terrains also affect the routing cost, because the connection between two coordinates are often impractical to be a straight Euclidean distance. The Earth is a globe shape with curvature and a terrain can have rugged floors or water surfaces that hinder the convenience of wiring. The objective of this paper is to take advantage of a developed heuristic and apply learning algorithms to determine the best wind power sites. The goal is to conserve wiring expense and accommodate power loads. Terrain knowledge is incorporated by utilizing the geographical databases. Experiments are conducted to demonstrate the approach and justify cost savings. It is expected that this paradigm can be expanded to address more factors and support other green energy application domains.
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U2 - 10.1016/j.procs.2016.09.317
DO - 10.1016/j.procs.2016.09.317
M3 - Conference article
AN - SCOPUS:84999006571
SN - 1877-0509
VL - 95
SP - 217
EP - 222
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - Complex Adaptive Systems, 2016
Y2 - 2 November 2016 through 4 November 2016
ER -