no code implementations • 16 Apr 2024 • Qiuyi Hong, Fanlin Meng, Felipe Maldonado
In the context of increasing demands for long-term multi-energy load forecasting in real-world applications, this paper introduces Patchformer, a novel model that integrates patch embedding with encoder-decoder Transformer-based architectures.
no code implementations • 30 Jun 2022 • Shuang Dai, Fanlin Meng, Qian Wang, Xizhong Chen
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
no code implementations • 7 Feb 2022 • Shuang Dai, Fanlin Meng
This survey explored OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning.
no code implementations • 25 Oct 2021 • Qian Wang, Fanlin Meng, Toby P. Breckon
The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes.
no code implementations • 8 Aug 2021 • Shuang Dai, Fanlin Meng, Qian Wang, Xizhong Chen
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
no code implementations • 3 Aug 2021 • Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen
To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature.
no code implementations • 10 Jun 2021 • Fanlin Meng, Qian Ma, Zixu Liu, Xiao-jun Zeng
In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the retail market.
1 code implementation • 1 Dec 2020 • Qian Wang, Fanlin Meng, Toby P. Breckon
As a result, our proposed methods (i. e. naive-SPL and norm-VAE-SPL) can achieve new state-of-the-art performance with the average accuracy of 93. 4% and 90. 4% on Office-Caltech and ImageCLEF-DA datasets, and comparable performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97. 2%, 87. 6% and 67. 9% respectively.
no code implementations • 18 Dec 2016 • Fanlin Meng, Xiao-jun Zeng, Yan Zhang, Chris J. Dent, Dunwei Gong
In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i. e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE).