  An accurate load forecasting has always been one of the main indispensable
parts in the operation and planning of power systems. Among different time
horizons of forecasting, while short-term load forecasting (STLF) and long-term
load forecasting (LTLF) have respectively got benefits of accurate predictors
and probabilistic forecasting, medium-term load forecasting (MTLF) demands more
attention due to its vital role in power system operation and planning such as
optimal scheduling of generation units, robust planning program for customer
service, and economic supply. In this study, a hybrid method, composed of
Support Vector Regression (SVR) and Symbiotic Organism Search Optimization
(SOSO) method, is proposed for MTLF. In the proposed forecasting model, SVR is
the main part of the forecasting algorithm while SOSO is embedded into it to
optimize the parameters of SVR. In addition, a minimum redundancy-maximum
relevance feature selection algorithm is used to in the preprocessing of input
data. The proposed method is tested on EUNITE competition dataset to
demonstrate its proper performance. Furthermore, it is compared with some
previous works to show eligibility of our method.
