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VMD-SSA-LSSVM-for-power-forecast

In this paper, LSSVM is used for short-term power load forecasting. At the same time, a Sparrow Search Algorithm (SSA) model is established to optimize the parameters of LLSVM to improve the forecasting accuracy. However, studies have shown that if a time series forecast model is built directly on the original series, the forecast data will lag the actual data. Such a model is meaningless. This is mainly due to the autocorrelation in the time series data, so I use variational model decomposition method(VMD) to decomposes the original sequence, eacn sub-component construsted ssa-lssvm model separately, and finally adds the results of each sub-mode's test set as the final result. The comparative analysis results show that the prediction accuracy of this model is better than that of many other prediction models, and this model shows better performance in short-term load forecasting. 本文将LSSVM用于短期电力负荷预测 , 提出基于LSSVM的短期电力负荷预测模型 , 同时建立基于麻雀算法(SSA)模型对LLSVM进行参数优化以提高预测精度。但研究表明,直接对原始序列建立时间序列预测模型,会出现预测数据滞后于实际数据,这样的模型没有意义,这主要是因为时间序列数据中存在的自相关性造成的,因此我采用VMD分解方法对原始序列进行分解,然后对每个序列分别建模,最后把各序列测试集的结果相加作为最终结果。对比分析结果显示,该模型的预测精度优于其他多种预测模型,该模型在短期负荷预测方面表现出较好的性能。详情见我博客:https://blog.csdn.net/qq_41043389/article/details/106035949 需要代码的加我qq2919218574 这个麻雀算法今年出来的,还么有在知网发表文章,可以水哦。代码是出售的,加我qq2919218574

项目简介

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源项目地址

https://github.com/fish-kong/vmd-ssa-lssvm-for-power-forecast

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