| 张小斐,耿俊成,万迪明,刘充许,周双喜.基于大数据平台的配电网负荷预测关键技术[J].电力需求侧管理,2020,22(4):25-30 |
| 基于大数据平台的配电网负荷预测关键技术 |
| Key technologies of load forecasting in distribution network based on big data platform |
| 投稿时间：2019-12-24 修订日期：2020-03-06 |
| DOI：DOI:10. 3969 / j. issn. 1009-1831. 2020. 04. 006 |
| 中文关键词: 配电网 负荷预测 大数据 流计算 机器学习 |
| 英文关键词: distribution network load forecasting big data stream computing machine learning |
| 基金项目:国家电网公司总部科技指南项目（5400-202024116A-0-0-00）；国网河南省电力公司电力科学研究院课题“配电网负荷预测模型研究及系统开发” |
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| 中文摘要: |
| 大规模配电网负荷预测需重点关注预测准确率及计算效率，由于规模大、关联因素复杂，采用传统集中式或简单分布式平台环境难以满足应用要求。提出了基于大数据平台的解决方案，方案结合大规模配电网负荷预测应用场景特点，提出了数据存储、数据预处理、特性分析、预测算法等关键环节的技术路线。应用多元化分布式存储方式，实现了多类型数据的分类存储；应用机器学习技术，实现了数据预处理、负荷特性分析等；应用Spark流计算技术，实现了基于滑动窗口操作的滚动短期预测和基于无状态操作的中长期预测。在某省的实际应用结果证明了解决方案在预测准确率和计算效率提升方面效果明显。 |
| 英文摘要: |
| Load forecasting of large scale distribution network needs to focus on the accuracy and calculation efficiency. Because of the large scale and complex correlation factors, the traditional centralized or simple distributed platform environment is difficult to meet the application requirements. A solution based on the big data platform is proposed. Combined with the characteristics of large-scale distribution network load forecasting application scenario, the technical route of key links such as data storage, data preprocessing, characteristic analysis, forecasting algorithm, etc. is proposed in the scheme. The classified storage of multiple types of data is realized by the application of diversified distributed storage mode；data preprocessing, load characteristic analysis, etc. is realized by the application of machine learning technology；rolling shortterm forecasting based on sliding window operation and medium and long-term forecasting based on stateless operation is realized by the application of spark stream computing technology. The practical application results in a province prove that the solution is effective in improving the forecasting accuracy and calculation efficiency. |
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