Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting.

电力需求预测在电力系统的运行中起着至关重要的作用，因为它可以提供与负荷切换和电网有关的管理决策。因此，已经开发了用于估计电力需求的模型。但是，不正确的需求预测可能会增加电力部门的运营成本，这将浪费大量资金。本文提出了一种新颖的电力需求预测建模框架。在通过分解技术消除了多余的噪声之后，开发了样本熵来识别原始时间序列中的非线性和不确定性。此外，通过特征选择方法确定了原始序列的最佳模式和模型的最佳输入形式。最后，通过多目标正弦余弦优化算法调整的最小二乘支持向量机可以对电力需求序列进行预测。澳大利亚的案例研究表明，提出的框架可以确保高精度和强大的稳定性。因此，可以将其视为电力需求预测的有用工具。