Aggregating Electric Load with a Complementary Data Clustering Technique

  • 馮 閔

Student thesis: Master's Thesis


In the electricity market the real-time balance of electricity generation and consumption is a main task In view of this power suppliers usually sign contracts capacity with their essential consumers (i e large-scale industrial and commercial companies) for managing their capacity demands With the electricity liberalization the characters called aggregators join to the electricity market and group consumers to integrate their demands to negotiate with power suppliers With a proper grouping of numerous consumers and aggregating their electricity load profile aggregators help to ensure stable and balance electric supply and reduce the burden of managing many consumers In this approach we propose a novel data clustering algorithm to cluster complementary consumers based on their daily load profile to damp the aggregation load Furthermore we incorporate the technique of discrete wavelet transform to speed up the clustering process Specifically approximations from only a few wavelet coefficients may precisely capture the shape of original usage patterns Experimental results based on a real dataset show that our approach is promising in practical applications
Date of Award2018 Sep 1
Original languageEnglish
SupervisorWei-Guang Teng (Supervisor)

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