Resumo:
The importance of short-term load forecasting has been increasing lately. With
deregulation and competition, energy price forecasting has become a big business. Bus-load
forecasting is essential to feed analytical methods utilized for determining energy prices. The
variability and non-stationarity of loads are becoming worse due to the dynamics of energy
prices. Besides, the number of nodal loads to be predicted does not allow frequent interactions
with load forecasting experts. More autonomous load predictors are needed in the new
competitive scenario.
This thesis deals with two main research lines. In the first one, two different strategies
for successfully embedding the Discrete Wavelet Transform into Artificial Neural Networksbased
short-term load forecasting is presented. The first strategy is new. It consists of creating
a model for load forecasting whose inputs are based on information from the original load
sequence and from wavelet domain subseries, as well. The second alternative predicts the
load’s future behavior by independently forecasting each subseries in the wavelet domain.
The other research line evaluates the feasibility of a nonlinear criterion based on the
method of delay coordinates for determining the best set of input variables for a neural
forecaster. This criterion is fully compared to another linear criterion based on the
autocorrelation function.
The main goal of this work is to develop more robust load forecasting algorithms.
Hourly load and temperature data for a North-American electric utility are used to test the
proposed methodologies.
