王茜 李皓然 王新娜 張媛媛

摘? ?要：短期負(fù)荷預(yù)測(cè)準(zhǔn)確性對(duì)于電網(wǎng)態(tài)勢(shì)感知和電網(wǎng)策略具有十分重要的意義。提出一種基于混沌類電磁學(xué)（CEM）優(yōu)化支持向量機(jī)的短期負(fù)荷預(yù)測(cè)方法，該方法利用聚類思想判斷數(shù)據(jù)質(zhì)量并進(jìn)行相關(guān)數(shù)據(jù)預(yù)處理工作。建立支持向量機(jī)的短期負(fù)荷預(yù)測(cè)模型，針對(duì)傳統(tǒng)支持向量機(jī)參數(shù)選擇困難問題，引入混沌類電磁學(xué)算法優(yōu)化參數(shù)，提高算法收斂效率和尋優(yōu)能力。仿真結(jié)果表明：所提算法較傳統(tǒng)支持向量機(jī)算法和粒子群-支持向量機(jī)算法（PSO-SVM）收斂速度更快，尋優(yōu)能力更強(qiáng)，適用于短期負(fù)荷預(yù)測(cè)。

關(guān)鍵詞：負(fù)荷預(yù)測(cè);類電磁學(xué);支持向量機(jī)

中圖分類號(hào)：TM796 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?文獻(xiàn)標(biāo)識(shí)碼：A

Short Term Load Forecasting Based on SVM

and Chaos Electromagnetic Algorithm

WANG Qian，LI Hao-ran？覮，WANG Xin-na，ZHANG Yuan-yuan

（Skills Training Center of State Grid Jibei Electric Power Company Limited

（Baoding Electric Power Voc.&Tech. College），Baoding，Hebei 071000，China）

Abstract：The accuracy of short-term load forecasting is very important for power grid situation awareness and power grid strategy. A short-term load forecasting method based on chaotic electromagnetics （CEM） optimization support vector machine （SVM） is proposed. This method uses clustering idea to judge the data quality and preprocess the related data. A short-term load forecasting model of SVM is established. Aiming at the difficult problem of parameter selection of traditional SVM，a new method is introduced. Chaotic electromagnetism algorithm optimizes parameters，and improves the convergence efficiency and optimization ability of the algorithm. Simulation results show that the proposed algorithm has faster convergence speed and stronger optimization ability than the traditional support vector machine algorithm and particle swarm optimization support vector machine （PSO-SVM），and is suitable for short-term load forecasting.

Key words：load forecasting;electromagnetics;support vector machine