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.

中文翻译：

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使用单乘法神经元模型、粒子群优化变体和非线性滤波器混合的在线短期负荷预测方法

短期负荷（STL）预测在现代电力系统管理中发挥着重要作用。提高STL预测精度有助于电力企业设计合理的运行计划，从而提高系统的经济效益和社会效益。本研究开发了由单乘法神经元（SMN）模型、各种粒子群优化（PSO）算法和非线性滤波器组成的混合方法。为此，首先利用SMN模型的权重和偏差以及SMN模型的输出建立基于SMN模型的非线性状态空间模型，以呈现状态向量和测量方程。然后使用 PSO 变体利用已知的 STL 训练数据来优化 SMN 模型的权重和偏差。最后，以训练阶段SMN模型的最优权重和偏差为初始化值，利用非线性滤波器对STL预测进行动态状态估计，STL预测结果用非线性滤波器的预测测量值表示。使用澳大利亚能源市场运营商的STL数据集测试了所提出方法的有效性，实验结果通过与其他方法相比证明了所提出方法的吸引力。