TY - GEN
T1 - Adaptive segmentation and machine learning based potential DR capacity analysis
AU - Tang, Wen Jun
AU - Wu, Yi Syuan
AU - Yang, Hong Tzer
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology, Taiwan, under Grant106-3113-E-006-010 & 104-2221-E-006 -116 -MY3.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049259864&partnerID=8YFLogxK
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U2 - 10.1109/ICHQP.2018.8378922
DO - 10.1109/ICHQP.2018.8378922
M3 - Conference contribution
AN - SCOPUS:85049259864
T3 - Proceedings of International Conference on Harmonics and Quality of Power, ICHQP
SP - 1
EP - 5
BT - ICHQP 2018 - 18th International Conference on Harmonics and Quality of Power
PB - IEEE Computer Society
T2 - 18th International Conference on Harmonics and Quality of Power, ICHQP 2018
Y2 - 13 May 2018 through 16 May 2018
ER -