Causal Markov Elman Network for Load Forecasting in Multinetwork Systems
Causal Markov Elman Network for Load Forecasting in Multinetwork Systems
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DOI：
10.1109/tie.2018.2851977
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发表时间：
2019-02
影响因子：
7.7
通讯作者：
Lalitha Madhavi Konila Sriram;Mostafa Gilanifar;Yuxun Zhou;Eren Erman Ozguven;R. Arghandeh
中科院分区：
文献类型：
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作者：
Lalitha Madhavi Konila Sriram;Mostafa Gilanifar;Yuxun Zhou;Eren Erman Ozguven;R. Arghandeh
本文提出了一种新的因果关系分析方法称为因果马尔可夫Elman网络（CMEN）来描述多网络系统中异构时间序列之间的相互依赖性。CMEN的性能，它包括通过马尔可夫属性过滤的输入，成功地刻画了城市环境中的各种多变量依赖关系。本文还提出了一个新的假设，表征互联系统，如电力和交通网络之间的联合信息。所提出的方法和假设，然后验证信息论距离为基础的度量。对于交叉验证，CMEN适用于电力负荷预测问题，使用实际数据从塔拉哈西，佛罗里达。
This paper proposes a novel causality analysis approach called the causal Markov Elman network (CMEN) to characterize the interdependence among heterogeneous time series in multinetwork systems. The CMEN performance, which comprises inputs filtered by Markov property, successfully characterizes various multivariate dependencies in an urban environment. This paper also proposes a novel hypothesis of characterizing joint information between interconnected systems such as electricity and transportation networks. The proposed methodology and the hypotheses are then validated by information theory distance-based metrics. For cross validation, the CMEN is applied to the electricity load forecasting problem using actual data from Tallahassee, Florida.