Abstract: Renewable energy systems are becoming a topic of
great interest and investment in the world. In recent years wind
power generation has experienced a very fast development in the
whole world. For planning and successful implementations of good
wind power plant projects, wind potential measurements are
required. In these projects, of great importance is the effective choice
of the micro location for wind potential measurements, installation of
the measurement station with the appropriate measuring equipment,
its maintenance and analysis of the gained data on wind potential
characteristics. In this paper, a wavelet transform has been applied to
analyze the wind speed data in the context of insight in the
characteristics of the wind and the selection of suitable locations that
could be the subject of a wind farm construction. This approach
shows that it can be a useful tool in investigation of wind potential.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.