In general, electricity prices reflect the cost to build, finance, maintain, and operate power plants and the electricity grid. Therefore, the cost-optimized scheduling of industrial loads with accurate price forecasts is very important. As such, recent studies have attempted to combine models to forecast electricity prices more accurately. Earlier combined models have tended to ignore the selection of sub-models and data analyses, leading to poor forecasting performance. In order to select the best forecasting models in a combined model, we propose a hybrid electricity price forecasting system that includes a data analysis module, a sub-model selection strategy module, optimized forecasting processing, and a model evaluation module. As such, the hybrid system fully exploits the advantages of a single model, thus improving the forecasting performance of the combined model. The experimental results show that the proposed system selects optimal sub-models effectively and successfully identifies future trend changes in the electricity price. Thus, the system can be an effective tool in the planning and implementation of smart grids.