Abstract The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting requirements of massive users. Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this article. By analyzing the characteristics of the Spark framework and the MapReduce framework, the Spark big data processing framework is determined as the basic framework for processing massive user load data, and a parallel K-means load clustering model based on Spark is designed. The on-line sequential learning machine OS-ELM makes the hidden layer data of computing each incremental training dataset mutually independent, therefore, a Spark-based parallel OS-ELM (SBPOS-ELM) algorithm is put forward. The proposed model is applied under the smart electricity big data environment and the training samples are selected using the incremental training dataset to make a short-term prediction of the millions of users’ smart meter electricity load, which verifies the feasibility and effectiveness of the proposed model. At last, comparing with other commonly used short-term load forecasting algorithms, the experimental results show that SBPOS-ELM algorithm has higher accuracy and operation efficiency.

中文翻译：

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基于Spark的并行OS-ELM算法在海量用户数据短期负荷预测中的应用

摘要 用户负荷数据的数据类型和数量呈指数级增长，传统的负荷预测方法难以满足海量用户的负荷预测需求。针对这一问题，本文提出了一种基于Spark的并行OS-ELM短期负荷预测模型。通过分析Spark框架和MapReduce框架的特点，确定Spark大数据处理框架为处理海量用户负载数据的基础框架，设计了基于Spark的并行K-means负载聚类模型。在线顺序学习机OS-ELM使得计算每个增量训练数据集的隐藏层数据相互独立，因此，提出了一种基于Spark的并行OS-ELM(SBPOS-ELM)算法。将该模型应用于智慧用电大数据环境下，利用增量训练数据集选取训练样本对百万用户智能电表用电负荷进行短期预测，验证了该模型的可行性和有效性。模型。最后，与其他常用的短期负荷预测算法相比，实验结果表明SBPOS-ELM算法具有更高的准确率和运行效率。