一种基于迁移学习的非平稳电力运检成本预测数学方法
A Mathematical Method for Operation and Maintenance Cost Prediction Based on Transfer Learning under Non-Stationary Power Data
DOI: 10.12677/AAM.2021.101012, PDF,  被引量    科研立项经费支持
作者: 潘 军, 陈 倩：国网浙江省电力有限公司金华供电公司，浙江 金华；金绍君*：国网浙江省电力有限公司，浙江 杭州
关键词: 迁移学习；循环神经网络；门控循环单元；非平稳；运检；电网；Transfer Learning； Recurrent Neural Networks； Gate Recurrent Units； Non-Stationary； Operation and Maintenance； Smart Grid
摘要: 电力行业是国家发展的重要能源产业，也是国民经济的第一基础产业。随着电网规模不断扩大，运行条件日益复杂，电力数据采集范围和频率不断增加，由于电力数据具有数据样本大、类型多、价值密度低等特征，如何合理运用电力大数据，高效快速挖掘有价值的信息，提高电力数据利用率，为电网运行的可靠性提供理论依据，满足实际需求，成为了一个新的研究热点。针对电力大数据的特点，本文利用时间序列、支持向量回归等人工智能方法，通过深度迁移学习，为标准成本预测任务建立数据挖掘网络模型，提取数据的关联性特征，提高数据预测的精度和效率。实验结果表明，本文模型在小样本数据集上得到较好的预测结果，验证了深度迁移模型的可行性，相比作业成本法、传统预测方法，本文方法平均绝对误差降低10%，具有有效性与优越性。
Abstract: The electric power enterprise is an important basic energy industry for national development, and it is also the first basic industry of the national economy. With the continuous expansion of State Grid, the progressively complex operating conditions, and the increasing scope and frequency of data collection, how to make reasonable use of electrical big data, improve utilization, and provide a theoretical basis for the reliability of State Grid operation, has become a new research hot spot. Since electrical data has the characteristics of large volume, multiple types, low value density, and fast processing speed, it is a challenge to mine and analyze it deeply, extract valuable information efficiently, and serve for actual problem. According to the features of these data, this paper uses artificial intelligence methods such as time series and support vector regression to establish a data mining network model for standard cost prediction through transfer learning. The experimental results show that the model in this paper obtains better prediction results on a small sample data set, which verifies the feasibility of the deep transfer model. Compared with activities based costing and the traditional prediction method, the average absolute error of the proposed method is reduced by 10%, which is effective and superior.
文章引用：潘军, 陈倩, 金绍君. 一种基于迁移学习的非平稳电力运检成本预测数学方法[J]. 应用数学进展, 2021, 10(1): 98-108. https://doi.org/10.12677/AAM.2021.101012

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