英文摘要 |
Thanks to advances in technologies, life is getting more and more convenient. However, it also comes with problems of increasing dependence on energy, as well as high electric demands in peak hours. Therefore, analysis of user behavior is necessary for utility companies to find potential improvements and make better strategies. This study is based on the data collected from Taiwan Power Company’s Meter Data Management System (MDMS). A data processing method to extract features of daily electricity consumption pattern is proposed and k-means clustering method is used to observe hidden user behavior patterns and the factors of behavior changes. Through this analysis, utility companies and grid administrators can manage the amount of electric demand to the level of household and thus can make specific strategies to alter user behaviors for the purpose of better grid safety or operational profits. In addition, the results of this analysis can also provide individualized and distinctive information to users for their interests of understanding themselves’ consuming behaviors and motivations of energy conserving. |