| 萤火虫算法优化SVR参数在短期电力负荷预测中的应用 |
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| 引用本文： | 唐宏,冯平,陈镜伯,陈杰睿,朱建疆.萤火虫算法优化SVR参数在短期电力负荷预测中的应用[J].西华大学学报(自然科学版),2017,36(1):35-38. |
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| 作者姓名： | 唐宏 冯平 陈镜伯 陈杰睿 朱建疆 |
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| 作者单位： | 1.后勤工程学院机械电气工程系，重庆 401311 |
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| 摘 要： | 由于萤火虫算法(FA)具有全局性能好、收敛精度高等优点，因此将萤火虫算法用于SVR的惩罚系数C和核参数σ的优化选取中。为提高迭代收敛速度和寻优精度，对萤火虫算法加以改进，在迭代过程中对亮度最亮的萤火虫位置施加随机扰动；将参数经过优化选取的SVR用于短期电力负荷预测，并将预测结果与采用网格搜索法、遗传算法、粒子群算法得到的结果做比较。其结果表明，采用改进萤火虫算法作参数寻优的SVR的负荷预测精度高，效果最好。
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| 关 键 词： | 萤火虫算法 支持向量机 电力负荷预测 |
| 收稿时间： | 2016-07-14 |
Application of Firefly Algorithm-Based Optimization of SVR Parameters in Short-term Power Load Forecasting |
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| TANG Hong,FENG Ping,CHEN Jingbo,CHEN Jierui,ZHU Jianjiang.Application of Firefly Algorithm-Based Optimization of SVR Parameters in Short-term Power Load Forecasting[J].Journal of Xihua University:Natural Science Edition,2017,36(1):35-38. |
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| Authors: | TANG Hong FENG Ping CHEN Jingbo CHEN Jierui ZHU Jianjiang |
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| Affiliation: | 1.Department of Mechanical and Electrical Engineering, Logistical Engineering University, Chongqing 401311 China |
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| Abstract: | Because firefly algorithm has such advantages as good global performance and high convergence precision, it is used to optimize the SVR penalty coefficient C and kernel parameter σ. A random disturbance is applied to the position of the brightest firefly in the iterative process to improve the original firefly algorithm, and a higher convergence rate and optimization accuracy are obtained. Optimized parameters are used for a short-term load forecasting to improve the prediction accuracy. This method is used to find the optimal parameters and make the regression forecast. Compared with grid search method, genetic algorithm and particle swarm optimization algorithm, the prediction results demonstrate modified firefly algorithm better than other several algorithms for parameter optimization. |
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