Xiaoxing Lu
North China Electric Power University
21 Papers
19 Citations
Xiaoxing Lu is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Computer science & Demand response. The author has an hindex of 6, co-authored 19 publications.
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Papers
Fundamentals and business model for resource aggregator of demand response in electricity markets
TL;DR: In this article, a comprehensive review of recent literature and projects is presented, with particular attention on RAs' roles in electricity markets as well as their difference from other market entities, and the business model for RA is analyzed systematically, involving resource aggregation, basic information prediction, market bidding strategy development, and settlement process.
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Deep Learning Based Surface Irradiance Mapping Model for Solar PV Power Forecasting Using Sky Image
Zhao Zhen,Jiaming Liu,Zhanyao Zhang,Fei Wang,Hua Chai,Yili Yu,Xiaoxing Lu,Tieqiang Wang,Yuzhang Lin +8 more
TL;DR: A hybrid mapping model based on deep learning applied for solar PV power forecasting has higher accuracy and can maintain robustness under different weather conditions and is proposed in this article.
202
Time–Frequency Feature Combination Based Household Characteristic Identification Approach Using Smart Meter Data
Siqing Yan,Kangping Li,Fei Wang,Xinxin Ge,Xiaoxing Lu,Zengqiang Mi,Hongyu Chen,Shengqiang Chang +7 more
TL;DR: This article proposes a time–frequency feature combination based household characteristic identification approach using smart meter data that shows better performance after incorporating the frequency-domain features.
83
Optimal Bidding Strategy of Demand Response Aggregator Based On Customers’ Responsiveness Behaviors Modeling Under Different Incentives
Xiaoxing Lu,Xinxin Ge,Kangping Li,Fei Wang,Hongtao Shen,Peng Tao,Junjie Hu,Jingang Lai,Zhao Zhen,Miadreza Shafie-khah,Joao P. S. Catalao +10 more
- 27 Apr 2021
TL;DR: An optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers is devised, which establishes the customers’ responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances.
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Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data
Fei Wang,Xiaoxing Lu,Xiqiang Chang,Xin Cao,Siqing Yan,Kangping Li,Neven Duić,Miadreza Shafie-khah,Joao P. S. Catalao +8 more
TL;DR: Case study on an Irish dataset indicates that the proposed semi-supervised learning approach outperforms supervised learning methods when only limited labeled data is available, and the identification accuracy improves with the increase of data resolution.
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