计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3317-3322.DOI: 10.11772/j.issn.1001-9081.2017.11.3317

• 应用前沿、交叉与综合 • 上一篇    下一篇

多算法多模型与在线第二次学习结合的短期电力负荷预测方法

周末, 金敏   

  1. 湖南大学 信息科学与工程学院, 长沙 410084
  • 收稿日期:2017-05-08 修回日期:2017-06-16 出版日期:2017-11-10 发布日期:2017-11-11
  • 通讯作者: 金敏
  • 作者简介:周末(1990-),男,湖北汉川人,硕士研究生,主要研究方向:人工智能、电力负荷预测;金敏(1973-),女,湖南岳阳人,教授,博士,主要研究方向:嵌入式系统、人工智能、大数据、工业4.0、电力负荷预测。
  • 基金资助:
    国家自然科学基金资助项目(61374172);国家科技成果转化项目(201255)。

Short-term power load forecasting method combining with multi-algorithm & multi-model and online second learning

ZHOU Mo, JIN Min   

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha Hunan 410084, China
  • Received:2017-05-08 Revised:2017-06-16 Online:2017-11-10 Published:2017-11-11
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61374172), the National Scientific and Technological Achievement Transformation Project of China (201255).

摘要: 为了提高短期电力负荷预测精度,首次提出多算法多模型与在线第二次学习结合的预测方法。首先,利用互信息方法和统计方法对输入变量进行选择;然后,通过Bootstrap方法对数据集进行多样性采样,利用多个不同的人工智能算法和机器学习算法训练得到多个差异化较大的异构预测模型;最后,用每个待预测时刻最近一段时间的实际负荷值、第一次学习生成的多异构预测模型的负荷预测值构成新训练数据集,对新训练数据集进行在线第二次学习,得到最终预测结果。对中国广州市负荷进行预测研究,与最优单模型、单算法多模型和多算法单模型相比,在每日总负荷预测中,全年平均绝对百分误差(MAPE)分别下降了21.07%、7.64%和5.00%,在每日峰值负荷预测中,全年MAPE分别下降了16.02%、7.60%和13.14%。实验结果表明,推荐方法有效地提高了负荷预测精度,有利于智能电网实现节能降耗、调度精细化管理和电网安全预警。

关键词: 短期电力负荷预测, 多样性采样, 异构模型, 多算法多模型, 在线第二次学习

Abstract: In order to improve the forecasting accuracy of the short-term power load, a forecasting method combining multi-algorithm & multi-model and online second learning was newly proposed. First, the input variables were selected by using mutual information and statistical information and a dataset was constructed. Then, multiple training sets were generated by performing diverse sampling with bootstrap on the original training set. Multiple models were obtained using different artificial intelligence and machine-learning algorithms. Finally, the offline second-learning method was improved. A new training set was generated using the actual load, and the multi-model forecasts for recent period within the forecasted time, which is trained by online second learning to obtain the final forecasting results. The load in Guangzhou, China was studied. Compared to the optimal single-model, single-algorithm & multi-model and multi-algorithm & single-model, Mean Absolute Percentage Error (MAPE) of the proposed model was reduced by 21.07%, 7.64% and 5.00%, respectively, in the daily total load forecasting, and by 16.02%, 7.60%, and 13.14%, respectively, in the daily peak load forecasting. The experimental results show that the proposed method can improve the prediction accuracy of the power load, reduce costs, implement optimal scheduling management, and ensure security with early warnings in smart grids.

Key words: short-term power load forecasting, diversity sampling, heterogeneous model, multi-algorithm and multi-model, online second learning

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