A Concept Drift Detection Method for Electricity Forecasting Based on Adaptive Window and Transformer
Author:
Affiliation:
1. Shandong Jianzhu University,School of Computer Science and Technology,Jinan,China
2. Shandong Normal University,School of Journalism and Communication,Jinan,China
Funder
National Natural Science Foundation of China
Taishan Scholar Project of Shandong Province
Natural Science Foundation of Shandong Province
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10447020/10448518/10449326.pdf?arnumber=10449326
Reference24 articles.
1. Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms
2. Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA
3. Short-term electric load forecasting in Tunisia using artificial neural networks
4. On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach
5. A survey on data preprocessing for data stream mining: Current status and future directions
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1. Concept drift adaptation with scarce labels: A novel approach based on diffusion and adversarial learning;Engineering Applications of Artificial Intelligence;2024-11
2. Temporal Attention for Few-Shot Concept Drift Detection in Streaming Data;Electronics;2024-06-03
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