|Abstract: ||以往文獻在預測住戶用電量時，概分兩類：(1)利用各式感測器(如動作感測器、溫濕度感測器、電量感測器等)先預測居民行為，進而從所使用的電器推估耗電量；(2)直接從感測器蒐集的數據推估未來用電量，然而預測準確度不盡理想。近年隨著人工智慧、深度學習技術的大幅躍進，例如Google旗下的DeepMind組織於2014年開始發展AlphaGo研究計畫，應用深度學習作法讓電腦學習下圍棋，並於2016年3月和世界冠軍韓國職業棋士進行對弈，結果為四勝一負。因此，本研究應用預測能力較好的深度學習演算法(Long Short-Term Memory,LSTM)，利用台灣有限的用電資料與英國公開的用電資料，分析智慧電表用電紀錄資料集合，並可以預測出住戶是否將有用電不安全情況，讓住戶有時間因應防範電氣火災可能發生的風險。|
另外，本研究分析英國、法國、瑞士、美國、澳洲及印度等國家公開的智慧電表用電資料集合，發現各國用電資料呈現的欄位定義及單位不同，未來可以再進一步針對台灣的智慧電表用電資料的欄位格式與定義做更進一步研究，以尋求智慧電表規格彈性化、用電資料國際共通性。;In the past, when predicting the electricity consumption of households, there are two categories: (1) using various types of sensors (such as motion sensors, temperature and humidity sensors, power sensors, etc.) to predict residents′ behaviors first, and then the electrical appliances used to estimate the final power consumption; (2) directly estimate the future electricity consumption from the data collected by the sensors, but both types of prediction accuracy are not satisfactory. In recent years, with the great leap of artificial intelligence and deep learning technology, for example, Google′s DeepMind organization began to develop the AlphaGo project in 2014, applying deep learning methods to let computers learn Go, and in March 2016 and played with the world champion. The game result was four wins (AlphaGo) and one loss. Therefore, this study applies the Long Short-Term Memory (LSTM) and uses the UK′s public electricity data as well as Taiwan′s limited power consumption data to analyze the power consumption records. It is assumed once households will encounter unsafe electricity usage conditions, residents can have more time to deal with the risks that may result in electrical fires.
In addition, this study analyzes the collection of smart meters used in countries such as the United Kingdom, France, Switzerland, the United States, Australia and India, and finds that the definitions and units of the fields used by countries′ electricity data are different. In the future, they can serve as the references for Taiwan′s smart meters deployment. By applying the proposed system, residents can stop their behaviors immediately if an alert is activated due to the unsafe electricity usage prediction.