Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine

Wenjie Guo, Jie Liu (), Jun Ma and Zheng Lan
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Wenjie Guo: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Jie Liu: School of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China
Jun Ma: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Zheng Lan: School of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China

Energies, 2025, vol. 18, issue 10, 1-17

Abstract: Accurate power load forecasting is crucial for ensuring grid stability, optimizing economic dispatch, and facilitating renewable energy integration in modern smart grids. However, real load forecasting is often disturbed by the inherent non-stationarity and multi-factor coupling effects. To address this problem, a novel hybrid forecasting framework based on adaptive mode decomposition (AMD) and improved least squares support vector machine (ILSSVM) is proposed for effective short-term power load forecasting. First, AMD is utilized to obtain multiple components of the power load signal. In AMD, the minimum energy loss is used to adjust the decomposition parameter adaptively, which can effectively decrease the risk of generating spurious modes and losing critical load components. Then, the ILSSVM is presented to predict different power load components, separately. Different frequency features are effectively extracted by using the proposed combination kernel structure, which can achieve the balance of learning capacity and generalization capacity for each unique load component. Further, an optimized genetic algorithm is deployed to optimize model parameters in ILSSVM by integrating the adaptive genetic algorithm and simulated annealing to improve load forecasting accuracy. The real short-term power load dataset is collected from Guangxi region in China to test the proposed forecasting framework. Extensive experiments are carried out and the results demonstrate that our framework achieves an MAPE of 1.78%, which outperforms some other advanced forecasting models.

Keywords: short-term power load forecasting; signal decomposition; least squares support vector machine; parameter optimization; renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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