Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm

机译：基于最小二乘支持向量机的中长期负载的组合预测模型及改装粒子群优化算法

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Mid-Long term load forecasting(MTLF) plays an important role in power system. With more factors involved, single forecasting method becomes hard to satisfy requirement. This paper proposes a new combination model for MTLF based on least squares support vector machines (LS-SVM) and particle swarm optimization (PSO) algorithm. LS-SVM is a new kind of SVM which regresses faster than standard, and a mended particle swarm optimization (MPSO) algorithm is employed to optimize the parameters of LS-SVM. With a real case test, the result shows proposed model outperforms tradition combination model.

机译：中长期负荷预测（MTLF）在电力系统中起着重要作用。涉及更多因素，单一的预测方法变得难以满足要求。本文提出了一种基于最小二乘支持向量机（LS-SVM）和粒子群优化（PSO）算法的MTLF的新组合模型。 LS-SVM是一种新的SVM，它比标准更快地回归，并且采用了修复的粒子群优化（MPSO）算法来优化LS-SVM的参数。通过实际案例测试，结果显示了提出的模型优于传统组合模型。

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《International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing》|2009年||共4页
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中图分类 TP18-53;
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LS-SVM; MPSO; Mid-long term forecasting; combination forecasting model;

机译：LS-SVM;MPSO;中长期预测;组合预测模型;

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专利

1. A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis [J] . Dongxiao Niu, Shuyu Dai Energies . 2017,第3期

机译：改进的粒子群优化算法和最小二乘支持向量机的经验模态分解和灰色关联度去噪短期负荷预测模型
2. Short-term Load Forecasting based on Support Vector Machine Optimized by Catfish Particle Swarm Optimization Algorithm [J] . Xiaoyan SHi, Zhenbi Li International Journal of Applied Mathematics & Statistics . 2013,第18aPta2期

机译：基于鲶鱼粒子群优化算法优化支持向量机的短期负荷预测
3. Short-term load forecasting by combination of artificial neural network and least squares-support vector machine algorithm optimized by harmony search algorithm optimized by random nelder-mead method [J] . Ming Zeng, Shulei Li, Xue Zhijie Wang, Energy education science and technology . 2012,第2期

机译：人工神经网络与最小二乘支持向量机算法相结合的短期负荷预测
4. Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm [C] . International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing . 2009

机译：基于最小二乘支持向量机的中长期负载的组合预测模型及改装粒子群优化算法
5. Application of least squares support vector machines in medium-term load forecasting. [D] . Afshin, Mohammadreza. 2007

机译：最小二乘支持向量机在中期负荷预测中的应用。
6. Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization [O] . Haifeng Xing, Bo Hou, Zhihui Lin, 2017

机译：基于最小二乘支持向量机的混沌粒子群优化的MEMS陀螺随机漂移建模与补偿
7. Short-Term Load Forecasting based on Variational Mode Decomposition and Least Squares Support Vector Machine by Improved Artificial Fish Swarm-Shuffled Frog Jump Algorithms [O] . Yang Haizhu, Jiang Zhaoyang, Li Menglong, 2019

机译：基于变分模式分解的短期负荷预测和改进的人工鱼类群组合青蛙跳跃算法的基于变分模式分解和最小二乘支持向量机

Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm

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