A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations
A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations
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一种改进物联网智能电表运行的机器学习决策支持系统
DOI：
10.1109/jiot.2017.2722358
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发表时间：
2017-06
影响因子：
10.6
通讯作者：
Joseph Siryani;Bereket Tanju;T. Eveleigh
中科院分区：
文献类型：
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作者：
Joseph Siryani;Bereket Tanju;T. Eveleigh
物联网(IoT)连接的社会和系统代表着巨大的范式转变。我们提出了一个在物联网生态系统内运行的决策支持系统(DSS)的框架。DSS利用对电力智能电表(ESM)网络通信质量数据的高级分析来改进智能电表现场操作的成本预测，并就是否向客户派遣技术人员来解决ESM问题提供可行的决策建议。该模型使用来自商业网络的数据集进行了经验性评估。我们用一个完整的贝叶斯网络预测模型证明了该方法的有效性，并与三种机器学习预测模型分类器进行了比较：1)朴素贝叶斯；2)随机森林；3)决策树。结果表明，我们的方法产生了统计上值得注意的估计，并且DSS将提高ESM网络运营和维护的成本效率。
An Internet of Things’ (IoT) connected society and system represents a tremendous paradigm shift. We present a framework for a decision-support system (DSS) that operates within the IoT ecosystem. The DSS leverages advanced analytics of electric smart meter (ESM) network communication-quality data to improve cost predictions for smart meter field operations and provide actionable decision recommendations regarding whether to send a technician to a customer location to resolve an ESM issue. The model is empirically evaluated using data sets from a commercial network. We demonstrate the efficiency of our approach with a complete Bayesian network prediction model and compare with three machine learning prediction model classifiers: 1) Naïve Bayes; 2) random forest; and 3) decision tree. Results demonstrate that our approach generates statistically noteworthy estimations and that the DSS will improve the cost efficiency of ESM network operations and maintenance.