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Short-term load (STL) forecasting plays a significant role in modern power system management. Improving the accuracy of STL forecasting is helpful for power enterprises to design reasonable operation planning, thus improving the economic and social benefits of the system. Hybrid methods consisted of single multiplicative neuron (SMN) model, various particle swarm optimization (PSO) algorithms and nonlinear filters are developed in this study. For this purpose, a SMN model based nonlinear state–space model is established using the weights and biases of SMN model and the output of SMN model to present the state vector and the measurement equation at first. Then PSO variants are used to optimize the weights and biases of SMN model with known STL training data. Finally, the nonlinear filters are employed to perform dynamic state estimation for STL forecasting by taking the optimal weights and biases of SMN model during training phase as the initialized value, and the STL forecasting results are represented by the predicted measurement value of nonlinear filters. The effectiveness of the suggested approaches is tested by using the STL datasets from Australian energy market operator, and the experimental results have demonstrated their attractiveness of the proposed methods by compared with other methods.