Medium-Term Load Forecasting Using Support Vector Regression, Feature Selection, and Symbiotic Organism Search Optimization
This addresses the need for accurate MTLF in power system operation and planning, such as scheduling and economic supply, but it is incremental as it builds on existing methods like SVR and optimization techniques.
The authors tackled medium-term load forecasting (MTLF) for power systems by proposing a hybrid method combining Support Vector Regression (SVR) with Symbiotic Organism Search Optimization (SOSO) and feature selection, achieving proper performance as tested on the EUNITE competition dataset.
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.