探勘智慧環境下電器之共相關行為模式及其應用

Abstract

基於現今感測儀器製作能力的先進，收集家中、商業大樓或大型工廠內的所有電力、水力及其他能源的日常使用資料已成為一件並不是技術上困難之事情。然而，對於這些居住者或是能源使用者而言，如何將這些能源的消耗利用適當的圖像化、圖表化等等視覺化技術呈現出來是一個不小的挑戰。因此，新的資料探勘演算法技術更需要發展並用以發現電能消耗模式。但是目前當代的模式探勘演算法主要聚焦在分析單一電器而鮮少去了解電器間的共相關行為模式。本論文中提出嶄新的演算法－CoPMiner－主要探勘使用模式及包含機率之共相關行為。裝備了三個創新的最佳化策略的CoPMiner演算法能夠有效並有效率的大幅減少探勘時間及搜索空間。再者，為了展示本演算法的實務及研究價值，我們將CoPMiner應用並探勘真實資料以顯現其電器之共相關行為模式。

Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this paper, a novel algorithm, namely, Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. Equipped with several new optimization techniques, CoPMiner can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.