A hybrid SARFIMA-FTS model for time series prediction in smart grids

The intensive use of electricity in life and modern society implies increasing demand and the need for increasingly high reliability. Smart Grids (SG) are the next technological breakthrough to be achieved for the generation, transmission and distribution of energy. Historically, it has been sought to automate each of these systems to perform main and ancillary services. Robust forecasting methodologies are essential for planning and operation of SG. However, SG data presents the characteristics of long memory time series, which are a kind of stationary processes in such a way that there is always a statistical long range dependency between the current value and values in different times of the series. Motivated by this, we introduce a new hybrid model combining Seasonal Auto Regressive Fractionally Integrated Moving Average with high order Fuzzy Time Series (SARFIMA-FTS). We present comparative results of SARFIMA-FTS with other two methodologies solutions in microgrid data. The computational results show that the performance of the proposed SARFIMA-FTS method is quite competitive with other presented methods in literature using less parameters, hence it is an important tool for prediction in smart grids.