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Load forecasting in a smart grid through customer behaviour learning using L1-regularized continuous conditional random fields

Conference Paper


Abstract


  • Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Load forecasting plays a critical role in Smart Grid. As there have been various types of customers with different behaviours in a Smart Grid, it would benefit load forecasting if customer behaviours were taken into consideration. This paper proposes a novel load forecasting method that efficiently explores customers' power consumption behaviours through learning. Our method uses L1-CCRF to initially learn the behaviour of each customer, followed by a hierarchical clustering process to cluster all the customers according to their different behaviour patterns, and then fine-tunes a corresponding L1-CCRF to predict the load for each customer cluster, and finally, sums all the predicted loads of customer clusters to obtain the load for the whole Smart Grid. The proposed method utilizes L1-CCRFs to effectively capture the relationships between various customers' loads and a range of outside influential factors. Experiments from different perspectives demonstrate the advantages of our load forecasting method through customer behaviour learning.

Publication Date


  • 2016

Citation


  • Wang, X., Zhang, M. & Ren, F. (2016). Load forecasting in a smart grid through customer behaviour learning using L1-regularized continuous conditional random fields. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS (pp. 817-825). ACM.

Scopus Eid


  • 2-s2.0-85014269928

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/142

Start Page


  • 817

End Page


  • 825

Abstract


  • Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Load forecasting plays a critical role in Smart Grid. As there have been various types of customers with different behaviours in a Smart Grid, it would benefit load forecasting if customer behaviours were taken into consideration. This paper proposes a novel load forecasting method that efficiently explores customers' power consumption behaviours through learning. Our method uses L1-CCRF to initially learn the behaviour of each customer, followed by a hierarchical clustering process to cluster all the customers according to their different behaviour patterns, and then fine-tunes a corresponding L1-CCRF to predict the load for each customer cluster, and finally, sums all the predicted loads of customer clusters to obtain the load for the whole Smart Grid. The proposed method utilizes L1-CCRFs to effectively capture the relationships between various customers' loads and a range of outside influential factors. Experiments from different perspectives demonstrate the advantages of our load forecasting method through customer behaviour learning.

Publication Date


  • 2016

Citation


  • Wang, X., Zhang, M. & Ren, F. (2016). Load forecasting in a smart grid through customer behaviour learning using L1-regularized continuous conditional random fields. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS (pp. 817-825). ACM.

Scopus Eid


  • 2-s2.0-85014269928

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/142

Start Page


  • 817

End Page


  • 825