Adaptive segmentation and machine learning based potential DR capacity analysis

Wen Jun Tang, Yi Syuan Wu, Hong Tzer Yang

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

By shedding their electricity consumption or getting ready to be shed, the Demand Response (DR) program compensates the shortage of generation or acts as spinning reserve. Depending on how the end-users join in the program. the potential capacity of DR is, therefore, a key issue no matter to system operator or DR aggregator. The proposed method employs adaptive k-means approach to evaluate the potential consumer as candidate participants of DR. The consumption prediction models of controllable appliances are constructed by Gaussian Processes for Machine Learning (GPML). Through combining the candidates' data and prediction models, the potential capacity is then achieved. Case studies evaluate the accuracy and efficacy of the proposed method with practical low voltage advanced metering infrastructure (LVAMI) data achieved from Taiwan. The results show good efficiency and practicability of the proposed method.

原文English
主出版物標題ICHQP 2018 - 18th International Conference on Harmonics and Quality of Power
發行者IEEE Computer Society
頁面1-5
頁數5
ISBN(電子)9781538605172
DOIs
出版狀態Published - 2018 六月 8
事件18th International Conference on Harmonics and Quality of Power, ICHQP 2018 - Ljubljana, Slovenia
持續時間: 2018 五月 132018 五月 16

出版系列

名字Proceedings of International Conference on Harmonics and Quality of Power, ICHQP
2018-May
ISSN(列印)1540-6008
ISSN(電子)2164-0610

Other

Other18th International Conference on Harmonics and Quality of Power, ICHQP 2018
國家Slovenia
城市Ljubljana
期間18-05-1318-05-16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Energy Engineering and Power Technology
  • Fuel Technology
  • Electrical and Electronic Engineering

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