计算机与现代化

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基于双图正则非负低秩分解的电力负荷短期预测

  

  1. (1.中国南方电网有限责任公司,广东广州510000;2.国电南瑞科技有限公司,江苏南京211106;
    3.云南电网有限责任公司德宏供电局,云南芒市678400)
  • 收稿日期:2019-10-12 出版日期:2020-05-20 发布日期:2020-05-21
  • 作者简介:梁寿愚(1975-),男,广东大埔人,教授级高级工程师,硕士,研究方向:云计算,大数据,人工智能,电力系统调度自动化应用分析,E-mail: liangsy@csg.cn; 方文崇(1986-),男,广西贵港人,高级工程师,硕士,研究方向:云计算,大数据,人工智能,电力系统调度自动化应用分析,E-mail: fangwc@csg.cn。
  • 基金资助:
    国家自然科学基金重大国际(地区)合作研究项目(61860206004)

Short-term Power Load Forecasting Method Based on Dual Graph #br# Regularized Non-negative Low-rank Matrix Decomposition

  1. (1. China Southern Power Grid, Guangzhou 510000, China; 2. Nari Technology Development Co., Ltd., Nanjing 211106, China;
    3. Dehong Power Supply Bureau, Yunnan Power Grid Co., Ltd., Mangshi 678400, China)
  • Received:2019-10-12 Online:2020-05-20 Published:2020-05-21

摘要: 在智能电网背景下,准确估算和预测电力负荷已成为电网电力规划工作的重要先决条件,对电网安全、经济运行具有重要意义。针对电力负荷数据的周期波动与非周期影响,提出一种基于双图正则非负低秩分解的电力负荷短期预测方法。该方法利用历史数据构造电力负荷时空矩阵,并对该矩阵进行鲁棒非负低秩矩阵分解,以同时获取电力负荷的周期性模式与非周期影响。在此基础上融入电力负荷的空间和时间相关性以进一步优化矩阵分解结果,最终通过矩阵恢复获取电力负荷的短期预测。该方法从电网时空整体预测电力负荷趋势分析并填补缺失,同时导出了有效的学习算法。实验分析表明,与相关方法相比,所提方法在电力负荷短期预测的多项评价标准下均取得了更好的准确性和鲁棒性。

关键词: 电力负荷预测, 矩阵补全, 低秩表示, 非负矩阵分解

Abstract: In the context of smart grids, accurate estimation and prediction of power load has become an important prerequisite for grid power planning, and is of great significance to grid operating safely and economically. Aiming at the cyclical fluctuation and non-periodic influence of power load data, a short-term power load forecasting method based on dual graph regularized non-negative low-rank decomposition is proposed. The method constructs the power load space-time matrix using historical data, and performs robust non-negative low-rank matrix decomposition on the matrix to simultaneously acquire the periodic mode and non-periodic influence of the power load. On this basis, the spatial and temporal correlation of the power load is integrated to further improve the matrix decomposition results, and finally the short-term prediction of the power load is obtained through matrix recovery. This method takes into account the missing complement and trend analysis of power load and derives an effective learning algorithm. The experimental analysis shows that compared with the related methods, the proposed method achieves better accuracy and robustness under multiple evaluation criteria of short-term forecasting of power load.

Key words: forecasting of power load, matrix completion, low-rank representation, non-negative matrix decomposition

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