A new neural network approach is applied to one-week ahead load foreca
sting. This approach uses a linear adaptive neuron or adaptive linear
combiner called ''Adaline''. An energy spectrum is used to analyze the
periodic components in a load sequence. The load sequence mainly cons
ists of three components: base load component, and low and high freque
ncy load components. Each load component has a unique frequency range.
Load decomposition is made for the load sequence using digital filter
s with different passband frequencies After load decomposition, each l
oad component can be forecasted by an Adaline. Each Adaline has an inp
ut sequence, an output sequence, and a desired response-signal sequenc
e. It also has a set of adjustable parameters called the weight vector
. In load forecasting. the weight vector is designed to make the outpu
t sequence, the forecasted load, follow the actual load sequence; it a
lso has a minimized Least Mean Square error. This approach is useful i
n forecasting unit scheduling commitments. Mean absolute percentage er
rors of less than 3.4 percent are derived from five months of utility
data, thus demonstrating the high degree of accuracy that can be obtai
ned without dependence on weather forecasts.