Analysis of Occupancy-Driven Power Consumption Data in Buildings Using Social Network Analysis

• Main Authors: Ru-Guan Wang, 王如觀
• Other Authors: Chien-Cheng Chou
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/rk2tsy

碩士 === 國立中央大學 === 土木工程學系 === 107 === Reducing carbon footprints in the building sector can be achieved by altering the power consumption behavior of building residents. Due to the influence of today’s declining birth rate and population aging, the structure of human society is changed, requiring the identification of key persons active in a community to persuade the others into saving electricity. This research aims at applying the technique of social network analysis (SNA) to a publicly available smart meter data set for building residents in Germany. Traditionally the head of a community can serve as the role of broadcasting energy-saving information, although its effectiveness varies with different circumstances. In the proposed SNA-based approach, the German data set is firstly examined and pre-processed, such as augmenting building occupancy data and relationships among residents. Then, different SNA indexes are explored in order to derive a generalized procedure for such identification of key persons. More sustainable societies can be established if key persons of a community can be identified and get involved by using the proposed approach. Energy-saving information specific to each type of home appliance can be broadcast effectively and efficiently, based on such identification, so that all building residents can implement the corresponding energy saving tips.