Long-Term Load Forecasting Using System Type Neural Network Architecture

This paper presents a methodology for long-term electric power demands using a semigroup based system-type neural network architecture. The assumption is that given enough data, the next year´s loads can be predicted using only components from the previous few years. This methodology is applied to recent load data, and the next year´s load data is satisfactorily forecasted. This method also provides a more in depth forecasted time interval than other methods that just predict the average or peak power demand in the interval.