以深度學習分析智慧電表資料的加值應用－以用電不安全預警為例

在節能減碳趨勢下，低電壓智慧電表逐漸普及。智慧電表收集資料除可用作耗電量預測促使節電，亦有其他加值應用。本研究探討用電不安全預警，以深度學習法，預測瞬間超高用電量與持續高用電量情形。英國智慧電表大數據為公開資料且品質佳，台灣智慧電表格式確定但尚在累積資料中，本研究比較兩地資料，構思一致方法與因地制宜參數，設計時間資料庫供演算法訓練，並評估英國用電不安全預警成效。台灣則以相同作法、本地資料，初探模型預警效能。待台灣智慧電表建置完善，套用本研究成果，預期能降低住戶電氣火災機率，提高智慧電表安裝意願。

關鍵字

深度學習；時間資料庫；智慧電表大數據分析；電氣火災

並列摘要

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.

並列關鍵字

deep learning ； temporal database ； smart meter data analytics ； electrical fire

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以深度學習分析智慧電表資料的加值應用－以用電不安全預警為例

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