Short-term electrical load forecasting using a fuzzy ARTMAP neural network

Accurate electrical load forecasting is a necessary part of resource management for power generating companies. The better the hourly load forecast, the more closely the power generating assets of the company can be configured to minimize the cost. Automation of this process is a profitable goal and neural networks have shown promising results in achieving this goal. The most often used neural network to solve the electric load forecasting problem is the backpropagation neural network architecture. Although the performance of the back-propagation neural network architecture has been encouraging, it is worth noting that it suffers from the slow convergence problem and the difficulty of interpreting the answers that the architecture provides. A neural network architecture that does not suffer fi om the above mentioned drawbacks is the Fuzzy ARTMAP neural network, developed by Carpenter, Grossberg, and their colleagues at Boston University. In this work we applied the Fuzzy ARTMAP neural network to the electric load forecasting problem. We performed numerous experiments to forecast the electrical. load. The experiments showed that the Fuzzy ARTMAP architecture yields as accurate electrical load forecasts as a back-propagation neural network with training time a small fraction of the training time required by the back-propagation neural network.