Short-term load forecasting (STLF) plays a significant role in economic and social development. As a challenging but indispensable task, STLF has become a hot topic in the field of energy. However, the inadequacy of existing methods lies in their inability to capture accurate input features that are highly related to the output, as the main focus of research has been on improving the accuracy of STLF, while ignoring its stability. Therefore, in this paper, a novel, robust hybrid forecasting system was developed, composed of four modules: (1) data preprocessing, (2) forecasting, (3) optimization, and (4) evaluation. In the data preprocessing module, an effective data preprocessing scheme based on singular spectrum analysis and gray correlation analysis was used to produce a smoother time series and to mine the best input and output structure for the model. An extreme learning machine (ELM) optimized using a multiobjective genetic algorithm (MOGA) that considers both the forecasting accuracy and stability was employed to provide the forecasting. Additionally, a generalized regression neural network (GRNN) was also used in the subsequent module to perform forecasting. Moreover, to further obtain accurate results and to overcome the drawbacks of using single models, a simulated annealing (SA) algorithm was utilized to optimize the combined parameters of the MOGA–ELM and GRNN algorithms in the optimization module. To validate the proposed model, half-hourly load data from the New South Wales and Tasmania are provided as illustrative cases. The experimental results show that the proposed hybrid model obtains more accurate and stable results than the reference models used for comparison.

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