Introduction of adaptive TS model using recursive Gustafson-Kessel algorithm in short term load forecasting

Abstract

This paper introduces adaptive TS model developed with upgraded recursive Gustafson-Kessel (rGK) clustering in the field of short-term load forecasting (STLF), which is one of the most essential parts for electrical distributors. The problem of STLF is to forecast load consumption for a day ahead based on the weather forecast and the type of the day. Until now, most of the forecasting methods based on fuzzy logic needed a lot of expert knowledge to build and adapt the model, where rGK clustering lowers the need of this expert knowledge because of the automatic partitioning of the domain. In addition to rGK clustering, proposed solution also moves from directly forecasting the average load to forecasting the change of load from current to the next day, which is the fastest way to adapt the model to the change in electrical load system. To improve domain separation of clustering, improved membership function based both on input and output distance is also proposed.