Boosting Latent Inference of Resident Preference from Electricity Usage - A Demonstration on Online Advertisement Strategies

Lo Pang Yun Ting, Po Hui Wu, Jhe Yun Jhang, Kai Jun Yang, Yen Ju Chen, Kun Ta Chuang

研究成果: Conference contribution

摘要

The electricity demand has increased due to the rapid development of the digital economy. The mechanisms of demand-side management are thus proposed to reduce the consumption while electricity companies forecast the appearance of excessive peak load which may incur the instability of electrical grids. However, DSM solutions are generally devised as the way of compulsively controlling home appliances but the interruption is not a pleasurable mechanism. To address this issue, we figure out an advertising strategy based on the residential electricity data acquired from smart meters. By recommending preference-related coupons to residents, we can induce residents to go outside to use the coupon while helping the peak load reduction with pleasure, leading to the win-win result between users and electricity companies. In this paper, we propose a novel framework, called DMAR, which combines the directed inference and the mediated inference to infer residents’ preferences based on their electricity usage. Through experimental studies on the real data of smart meters from the power company, we demonstrate that our method can outperform other baselines in the preference inference task. Meanwhile, we also build a line bot system to implement our advertisement service for the real-world residents. Both offline and online experiments show the practicability of the proposed DMAR framework.

原文English
主出版物標題Big Data Analytics and Knowledge Discovery - 23rd International Conference, DaWaK 2021, Proceedings
編輯Matteo Golfarelli, Robert Wrembel, Gabriele Kotsis, A Min Tjoa, Ismail Khalil
發行者Springer Science and Business Media Deutschland GmbH
頁面272-279
頁數8
ISBN(列印)9783030865337
DOIs
出版狀態Published - 2021
事件23rd International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2021 - Virtual, Online
持續時間: 2021 九月 272021 九月 30

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12925 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference23rd International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2021
城市Virtual, Online
期間21-09-2721-09-30

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)

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