A fuzzy methodology to improve time series forecast of power demand in distribution systems

This paper aims to introduce a methodology for choosing the best inputs and tuning a multilayer fuzzy inference system dedicated to estimate future time series power demand values in a substation feeder. On an iteration process, older data with greater correlation with the previous forecast errors are the inputs of the fuzzy system, which has as output a future demand value. It is attempted to estimate the largest possible horizon reaching the minimum forecast error. The obtained results are satisfactory, showing that the developed methodology is capable of picking a small number of inputs to forecast with accuracy different horizons.