A Comprehensive Learning-Based Model for Power Load Forecasting in Smart Grid
A Comprehensive Learning-Based Model for Power Load Forecasting in Smart Grid
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DOI：
10.4149/cai_2017_2_470
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Huifang Li;Yidong Li;Hai-rong Dong
中科院分区：
文献类型：
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作者：
Huifang Li;Yidong Li;Hai-rong Dong
在大数据时代，基于学习的技术在智能电网、智能交通等行业领域受到越来越多的关注。电力负荷预测是智能电网数据分析的关键问题之一。然而，基于学习的方法并没有被广泛使用，由于数据质量和计算能力差。在本文中，我们提出了一个综合的基于学习的模型来预测重载（HOL）事故，根据各种信息系统的数据。首先，我们提出了一种适用于不平衡电力数据的组合随机欠采样和过采样技术，并通过多次实验选择了最佳采样率。然后，我们约简的属性，有显着的影响，电力负荷，通过使用基于学习的方法。最后，我们提出了一个基于随机森林方法的算法来防止过拟合问题。我们评估所提出的模型和算法与中国电网提供的真实世界的数据。实验结果表明，我们的模型有效地工作，并实现低错误率。
In the big data era, learning-based techniques have attracted more and more attention in many industry areas such as smart grid, intelligent transportation. The power load forecasting is one of the most critical issues in data analysis of smart grid. However, learning-based methods have not been widely used due to the poor data quality and computational capacity. In this paper, we propose a comprehensive learning-based model to forecast heavy and over load (HOL) accidents according to the data from various information systems. At first, we present a combined random under- and over-sampling technique for imbalanced electric data, and choose an optimal sampling rate through several experiments. Then, we reduce the attributes that have significant impact on the power load by using learning-based methods. Finally, we provide an algorithm based on the random forest method to prevent the over-fitting problem. We evaluate the proposed model and algorithms with the real-world data provided by China Grid. The experimental results show that our model works efficiently and achieves low error rates.