| 岳 友,李宽宏.基于配电物联网技术和并行随机森林算法的日前V2G功率容量预测[J].电力需求侧管理,2020,22(4):31-34 |
| 基于配电物联网技术和并行随机森林算法的日前V2G功率容量预测 |
| Day⁃ahead prediction of V2G power capacity based on distribution Internet of Things technology and parallel random forest algorithm |
| 投稿时间：2019-12-24 修订日期：2020-03-26 |
| DOI：DOI:10. 3969 / j. issn. 1009-1831. 2020. 04. 007 |
| 中文关键词: 配电物联网 V2G功率容量 并行预测 |
| 英文关键词: distribution Internet of Things V2G power capacity parallel prediction |
| 基金项目:国电南瑞科技项目（524609190013） |
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| 摘要点击次数: 1523 |
| 全文下载次数: 685 |
| 中文摘要: |
| 实现大量电动汽车所聚合电池储能作为电网和可再生能源的缓冲，关键是精确和快速的 V2G 功率容量预测。然而，电动汽车充放电行为的随机性和不确定性，给精确的V2G功率预测带来了困难。随着配电物联网建设推进和电动汽车的大规模接入，电动汽车充放电数据爆发式增长，为精度的V2G功率容量提供大量数据支撑的同时，也带来了大数据处理问题。通过利用大量历史和气象数据构建预测特征向量，基于并行随机森林算法建立了日前V2G功率容量的并行预测模型。从而，避免了用户行为对预测结果的影响。此外，基于Spark搭建了分布式的大数据平台，以实现V2G功率容量快速预测。最终，所提出的方法与利用SVM算法在传统单机平台上预测V2G功率容量进行对比分析。试验结果表明采用并行随机森林算法不仅精度比传统SVM高1.75%，速度还要快6倍多。 |
| 英文摘要: |
| Accurate and fast prediction of V2G power capacity is the key to realize the energy storage of aggregated batteries of electric vehicles（EVs）as a buffer for power grid and renewable energy. However, the randomness and uncertainty of charging and discharging behavior of electric vehicles make it difficult to predict V2G power accurately. With the development of the Internet of Things（IOT）and the large-scale access of electric vehicles, the charging and discharging data of electric vehicles have increased explosively, which provides a large amount of data support for the accurate V2G power capacity, but also brings large data processing problems. A parallel prediction model of V2G power capacity is established based on the parallel random forest algorithm by using a large amount of historical and meteorological data to construct the prediction feature vector. Thus, the influence of user behavior on prediction results is avoided. In addition, a distributed big data platform based on Spark is built to realize rapid prediction of V2G power capacity. Finally, the proposed method is compared with SVM algorithm which is used in predicting V2G power capacity on traditional stand-alone platform. The experimental results show that using the parallel random forest algorithm is not only 1.75% more accurate than using the traditional SVM, but also more than 6 times faster. |
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