| Summary: | Wind power is the safest, cleanest resource and has emerged as the speediest
growing renewable energy in terms of annual installed capacity. Before a wind-drive
system is set up, thorough wind resource assessment (WRA) must be conducted. In this
paper, a methodology based on ground-station and topographical neural network
modeled data is proposed to study the wind energy potential, in the monitored location
and areas not covered by directly measurement instrumentation at Kuching. A new
topographical feed forward neural network (T-FFNN) back propagation trained with
Levenberg-Marquardt (LM), which consists of three layers was used to model the wind
speed profile. The daily 10 m height, average hourly measured wind speed data for a
period of ten years (2003-2012) for eight stations operated by Malaysia Meteorological
Department (MMD) were used for the training, testing and validation. The
geographical, meteorological and synthesized topographical parameters were used as
input data, whereas the monthly wind speeds as the objective function. The optimum
topology with maximum mean absolute percentage error of 6.4 % and correlation value
of 0.9946 between the reference measured and predicted was obtained. The predicted
monthly wind speed varied from 1.3-1.98 m/s with an average annual wind speed of
1.62 m/s. The characteristics of ground -based station was analyzed and presented. It
was found in all the areas examined that the wind power falls within a low power
density class (PD ≤ 100w/m2
). Results from the micro-sizing showed an annual energy
output (AEO) in the range of 4-12 MWh/year.
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