| 基于支持向量机的船舶电力负荷预测 |
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| 引用本文： | 王锡淮,朱思锋.基于支持向量机的船舶电力负荷预测[J].中国电机工程学报,2004,24(10):36-39. |
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| 作者姓名： | 王锡淮 朱思锋 |
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| 作者单位： | 上海海事大学,上海,200135 |
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| 基金项目： | 国家自然科学基金项目(60074004)，上海市教育委员会科研重点项目(04FA02)~~ |
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| 摘 要： | 船舶电力系统是一个独立的电力系统，需要根据准确的负荷预测来控制多台发电机组的运行。本文提出了一种基于支持向量机的船舶电力负荷短期预测方法。对某大型集装箱船舶在不同工况下的电力负荷数据，分别用基于径向基核函数的支持向量机方法、多层BP网络和RBF网络方法进行训练和预测计算，仿真结果表明支持向量机具有更高的预测精度，是船舶电力负荷预测的一种有效方法。
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| 关 键 词： | 电力负荷预测 发电机组 径向基 运行 有效方法 工况 机具 支持向量机 仿真结果 RBF网络 |
| 文章编号： | 0258-8013(2004)10-0036-04 |
| 修稿时间： | 2004年3月21日 |
SHIP POWER LOAD FORECASTING USING SUPPORT VECTOR MACHINE |
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| WANG Xi-huai,ZHU Shi-feng.SHIP POWER LOAD FORECASTING USING SUPPORT VECTOR MACHINE[J].Proceedings of the CSEE,2004,24(10):36-39. |
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| Authors: | WANG Xi-huai ZHU Shi-feng |
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| Abstract: | Ship power system is an isolated power system, and the several generators are controlled to run or stop according to accurate load forecasting respectively. A new short-term load forecasting method for ship power system based on support vector machine (SVM) is presented. Three methods of the load forecasting, the SVM based on radial basis function kernel, the multi-layer back-propagation neural network and the radial basis function neural network, are compared for actual load data sampled at different operation modes from a large-scale container ship. The simulation results show that the SVM method can achieve greater accuracy than other methods, and is effective for ship power load forecasting. |
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| Keywords: | Electric power engineering Electric power engineering Support vector machine Machine learning Ship power system Load forecasting |
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