Fig 1.
The HMN-RTS framework, a hierarchical spatiotemporal multiplex network, is designed for multi-modal prediction of power outage duration.
Table 1.
The distribution of power forced outage durations in the BPA service territory during 2021-2022 is divided into five distinct duration classes.
Table 2.
Multiplex network topological structure. L= number of layer, V= number of nodes, E= number of total edges, = number of coupling edges, avg(DC)=average Degree Centrality, avg(CC)=average Closeness Centrality, avg(EC)=average Eigenvector Centrality, avg(CF) Clustering Coefficient, avg(SCF)=average Square Clustering.
Table 3.
A comparison of macro precision, macro recall, and macro F1 score is conducted across various models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), the Reddit and Twitter Multiplex Network (RTMNO), the Hierarchical One-Layer Network Model (HON-RTS), and the Hierarchical Multiplex Network Model (HMN-RTS). The outage duration is categorized into five classes, as outlined in Table 1.
Fig 2.
The normalized confusion matrix for the LSTM model illustrates its performance in predicting outage durations.
Fig 3.
The normalized confusion matrix for the GRUs model illustrates its performance in predicting outage durations.
Fig 4.
The normalized confusion matrix for the RTMNO model illustrates its performance in predicting outage durations.
Fig 5.
The normalized confusion matrix for the HON-RTS model illustrates its performance in predicting outage durations.
Fig 6.
The normalized confusion matrix for the HMN-RTS model illustrates its performance in predicting outage durations.
Table 4.
The ablation study of the HMN-RTS model investigates the contributions of its individual components. The Multiplex Network Structure includes the layers outlined in Sect 3.2, while Reddit and Twitter represent the activities captured from social sensors.
Fig 7.
The performance of the HMN-RTS model in the early detection of outages is evaluated using the macro F1 score for a five-class problem, as detailed in Table 1.
The X-axis in the corresponding figure shows the number of hours before the power outage at which predictions are made.
Fig 8.
Percentage of power outages by season during 2021–2022.
Table 5.
A comparison of the average macro F1 scores for two seasonal models (Winter and Summer) using the Hierarchical Multiplex Network Model (HMN-RTS). Note that the standard deviations in all models are between [0.08,0.1].