基於巨量資料分析電器使用時間與衰退曲線之關聯性

Explore The Correlation Between Appliance Use Time and Decline Curve Based on Big Data

侯承育(Cheng-Yu Hou)

指導教授 ： 賴槿峰

國立中正大學/工學院/資訊工程學系/碩士(2016年)

摘要

由於萬物聯網技術日漸成熟，在應用範圍與使用者快速擴展的狀況下，所產生的電力資訊也會急速上升，在長時間資料收集與資料運算分析應用上，需要透過巨量資料方法解決。為解決上述問題，本論文主要目的在採用RHadoop建立貝氏迴歸模型，收集物聯網之電力資料，轉換成不同使用時間下的電力特徵值，經由狀態判別方法後利用電力特徵值為自變數，使用時間為因變數，建立衰退曲線，最後透過評分與評估模組，找出最適合的電力特徵值為自變數。將其技術應用於探討使用時間與衰退曲線的關聯性與預測使用時間，藉以提升整體智慧家庭行為分析與電器使用時間辨識。

關鍵字

萬物聯網 ； 電器辨識 ； 電力特徵値

並列摘要

Due to the fast development of Internet of Everything, there is a rapid rise in the electronic data producing by appliances. For long time data collection, data operation, data analysis and applications will cause big data. To solve these problems, the main purpose of this paper is using RHadoop to build Bayesian regression model. The appliance data are collected from smart meter, and converted into power features. After identifying the state of power data by the state identification method, the system will build regression model. The dependent variable is appliance use time (weeks) and the independent variable is power feature. The score model and evaluate model is to decide which power feature is most suitable for being independent variable at last. The technology is used to explore the correlation between appliance use time and decline curve and to predict appliance use time, in order to enhance the overall behavior analysis in Smart Home.

並列關鍵字

IoE ； appliance recognition ； power feature

參考文獻

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