Load Forecasting Based on Variational Mode Decomposition and Sparrow Search Algorithm Optimized LSSVM [VMD-SSA-LSSVM] [Multivariate]

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📋📋📋 The content of this article is as follows: 🎁🎁🎁

Directory

💥1 Overview

1.1 VMD Variational Mode Decomposition

1.2 LSSVM Least Squares Support Vector Machine

1.3 SSA Sparrow Search Algorithm

📚2 Results

2.1 LSSVM

2.2 Sparrow Optimized LSSVM

2.3 Variational Mode Decomposition Optimized LSSVM

2.4 Variational Mode Decomposition and Sparrow Optimized LSSVM (VMD-SSA-LSSVM)

🎉3 References

🌈4 Matlab Code, Data, Article Explanation

1 Overview

Source:

In response to the issues of low prediction accuracy caused by the randomness, volatility, and non-linear factors of power load, a short-term load forecasting model based on Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA) optimized Least Squares Support Vector Machine (LSSVM) is proposed. This method first uses VMD to decompose the original load time series into different frequency intrinsic mode functions (IMF) and residual components (Res), then establishes different LSSVM prediction models for each component and uses SSA for parameter optimization. Finally, the predicted values of each component are combined to obtain the final prediction result. The prediction results are compared with the predicted values of LSSVM, VMD-LSSVM, SSA-LSSVM, and VMD-SSA-LSSVM models, yielding MAPE values of 1.5016% and 4.765%, significantly lower than those of other models. The results indicate that the proposed combined forecasting model has certain advantages in prediction accuracy.

1.1 VMD Variational Mode Decomposition

During the load data collection process, the power sector is subject to various electromagnetic noise interferences, which severely affect data quality and lead to low prediction accuracy. Therefore, it is necessary to perform data denoising. VMD is an adaptive decomposition method for non-stationary signals that can determine the number of modes to decompose based on the actual situation of the sequence and can adaptively match the optimal center frequency bandwidth for each type of mode during the solving process, ultimately obtaining the optimal solution to the problem.

1.2 LSSVM Least Squares Support Vector Machine

LSSVM is an improvement on SVM, which uses a least squares linear equation as its loss function, replacing the inequality constraints in standard support vector machines with equality constraints. This transforms the quadratic programming problem into solving a system of linear equations, simplifying the computational complexity and improving the convergence speed of the algorithm, which helps effectively fit the short-term power load with non-linear characteristics. The specific steps are as follows. Let a given set of training samples be:

The core principle of LSSVM is to map the training samples into a high-dimensional feature space through non-linear mapping, and then perform linear regression in the high-dimensional space. The regression function can be described as follows:

1.3 SSA Sparrow Search Algorithm

The Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm proposed by Xue et al. in 2020. It is a heuristic algorithm that mimics the cooperative behavior of sparrow populations during the predation process, which enhances the exploration and development of the optimal search space to some extent. The detailed steps are as follows.

2 Results

2.1 LSSVM2.2 Sparrow Optimized LSSVM 2.3 Variational Mode Decomposition Optimized LSSVM

2.4 Variational Mode Decomposition and Sparrow Optimized LSSVM (VMD-SSA-LSSVM)

3References

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[1] Yang Deng, Yang Junjie, Hu Chenyang, Cui Dan, Chen Zhaoguang. Short-term power load forecasting based on improved LSSVM [J]. Electronic Measurement Technology, 2021, 44(18): 47-53. DOI:10.19651/j.cnki.emt.2107628.

[2] Wu Xiaotao, Yuan Xiaohui, Yuan Yanbin, Mao Yaqian, Xiao Jiaqing. Daily runoff prediction during flood season based on EEMD-VMD-SSA-KELM model [J/OL]. China Rural Water and Hydropower: 1-14 [2023-04-09].

4 Matlab Code, Data, Article Explanation

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