As there is an increasing number of countries worldwide to start large-scale deployments of smart meters to promote energy conservation and carbon reduction, residents' willingness is a factor that cannot be ignored. This research proposes a new type of smart meter data analytics that can provide residents with early warning of possible electrical fires, in order to prevent disasters to increase their enrolment and adoption of smart meters. Additionally, since the smart meter open data sets from UK are relatively complete, and because smart meters in Taiwan are currently being deployed, this study first compares the data formats of smart meters from the two countries and designs a consistent, deep learning-based algorithm that can be utilized for both UK and Taiwan cases. A temporal database is created to serve as the training data source, and such early warning models are then developed and tested. The results show that once a sufficient number of electricity consumption records are available, the proposed approach can predict whether there will be any instantaneously or continuously abnormal electricity consumption events during the next several hours. The prediction accuracy for such unsafe electricity usage is above 70%. As for the Taiwan case, with appropriate parameters adjustment and customization efforts, it can be expected that the proposed approach can help Taiwan residents detect possible electrical fires by using their smart meter data sets in order to ensure their safety of life and property.
|Translated title of the contribution||Deep Learning-Based Smart Meter Data Analytics for Early Warning of Possible Electrical Fires|
|Original language||Chinese (Traditional)|
|Number of pages||12|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|State||Published - 1 Apr 2019|