An Adaptive Wavelet-Network Model for Forecasting Daily Total Solar-Radiation
Applied Energy, (2006)Abstract
The combination of wavelet theory and neural networks has lead to
the development of wavelet networks. Wavelet-networks are feed-forward
networks using wavelets as activation functions. Wavelet-networks
have been used successfully in various engineering applications such
as classification, identification and control problems. In this paper,
the use of adaptive wavelet-network architecture in finding a suitable
forecasting model for predicting the daily total solar-radiation
is investigated. Total solar-radiation is considered as the most
important parameter in the performance prediction of renewable energy
systems, particularly in sizing photovoltaic (PV) power systems.
For this purpose, daily total solar-radiation data have been recorded
during the period extending from 1981 to 2001, by a meteorological
station in Algeria. The wavelet-network model has been trained by
using either the 19 years of data or one year of the data. In both
cases the total solar radiation data corresponding to year 2001 was
used for testing the model. The network was trained to accept and
handle a number of unusual cases. Results indicate that the model
predicts daily total solar-radiation values with a good accuracy
of approximately 97% and the mean absolute percentage error is not
more than 6%. In addition, the performance of the model was compared
with different neural network structures and classical models. Training
algorithms for wavelet-networks require smaller numbers of iterations
when compared with other neural networks. The model can be used to
fill missing data in weather databases. Additionally, the proposed
model can be generalized and used in different locations and for
other weather data, such as sunshine duration and ambient temperature.
Finally, an application using the model for sizing a PV-power system
is presented in order to confirm the validity of this model.