Neural Network-Based Approaches to Nonlinear Short Term LoadForcasting for Electric Utilities.

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Electric load demand is influenced in a highly complex manner by several variables such as time of day, day of the week, and weather variables such as temperature, humidity and wind speed. The research proposed is to investigate the application of three different methods for applying neural network methodology to the problem of short term load forecasting. The first approach will be a feedforward neural network using backpropagation. A second approach will attempt to forecast using a neural network architecture using linear threshold units. The third approach will use a neural network to model nonlinear system and weather dynamics using ideas drawn from deterministic chaos theory. These approaches will be tested on actual load and weather data provided by a cooperating utility.