Implementation of an adaptive neural network short-term electric load forecasting system in the energy control center

The practical aspects related to the implementation of an adaptive neural network based short-term electric load forecasting system in a utility company are presented. The system is developed and implemented for Florida Power and Light Company (FPL). Implementation experiences with the system are discussed. The system accounts for seasonal and daily characteristics, as well as abnormal conditions such as cold fronts, heat waves, holidays and other conditions abnormal conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism is used to train the neural networks when online. The results indicate that the load forecasting system presented gives robust and more accurate forecasts and allows greater adaptability to sudden climatic changes compared with statistical methods. With appropriate training data sets, the system could be ported and can be modified to suit the requirements of other utility companies