Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | ICHQP 2018 - 18th International Conference on Harmonics and Quality of Power |
| Publisher | IEEE Computer Society |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538605172 |
| DOIs | |
| Publication status | Published - 2018 Jun 8 |
| Event | 18th International Conference on Harmonics and Quality of Power, ICHQP 2018 - Ljubljana, Slovenia Duration: 2018 May 13 → 2018 May 16 |
Publication series
| Name | Proceedings of International Conference on Harmonics and Quality of Power, ICHQP |
|---|---|
| Volume | 2018-May |
| ISSN (Print) | 1540-6008 |
| ISSN (Electronic) | 2164-0610 |
Other
| Other | 18th International Conference on Harmonics and Quality of Power, ICHQP 2018 |
|---|---|
| Country | Slovenia |
| City | Ljubljana |
| Period | 18-05-13 → 18-05-16 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Signal Processing
- Energy Engineering and Power Technology
- Fuel Technology
- Electrical and Electronic Engineering
Cite this
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Adaptive segmentation and machine learning based potential DR capacity analysis. / Tang, Wen Jun; Wu, Yi Syuan; Yang, Hong-Tzer.
ICHQP 2018 - 18th International Conference on Harmonics and Quality of Power. IEEE Computer Society, 2018. p. 1-5 (Proceedings of International Conference on Harmonics and Quality of Power, ICHQP; Vol. 2018-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
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
UR - http://www.scopus.com/inward/citedby.url?scp=85049259864&partnerID=8YFLogxK
U2 - 10.1109/ICHQP.2018.8378922
DO - 10.1109/ICHQP.2018.8378922
M3 - Conference contribution
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
ER -