Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) |
| Editors | Satinder Singh, Shaul Markovitch |
| Place of Publication | Palo Alto CA USA |
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
| Pages | 4935-4936 |
| Number of pages | 2 |
| Publication status | Published - 2017 |
| Event | AAAI Conference on Artificial Intelligence 2017 - Hilton San Francisco Union Square, San Francisco, United States of America Duration: 4 Feb 2017 → 10 Feb 2017 Conference number: 31st http://www.aaai.org/Conferences/AAAI/aaai17.php |
Conference
| Conference | AAAI Conference on Artificial Intelligence 2017 |
|---|---|
| Abbreviated title | AAAI 2017 |
| Country | United States of America |
| City | San Francisco |
| Period | 4/02/17 → 10/02/17 |
| Internet address |
Cite this
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Fast electrical demand optimization under real-time pricing. / He, Shan; Wallace, Mark; Wilson, Campbell; Liebman, Ariel.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). ed. / Satinder Singh; Shaul Markovitch. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2017. p. 4935-4936.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other
TY - GEN
T1 - Fast electrical demand optimization under real-time pricing
AU - He, Shan
AU - Wallace, Mark
AU - Wilson, Campbell
AU - Liebman, Ariel
PY - 2017
Y1 - 2017
N2 - The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.
AB - The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.
UR - http://www.scopus.com/inward/record.url?scp=85030458024&partnerID=8YFLogxK
M3 - Conference Paper
AN - SCOPUS:85030458024
SP - 4935
EP - 4936
BT - Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
A2 - Singh, Satinder
A2 - Markovitch, Shaul
PB - Association for the Advancement of Artificial Intelligence (AAAI)
CY - Palo Alto CA USA
ER -