SADI: A Self-Adaptive Decomposed Interpretable Framework for Electric Load Forecasting Under Extreme Events

Author:

Liu Hengbo1,Ma Ziqing1,Yang Linxiao1,Zhou Tian1,Xia Rui1,Wang Yi2,Wen Qingsong3,Sun Liang3

Affiliation:

1. Alibaba Group,DAMO Academy,Hangzhou,China

2. The University of Hong Kong,Hong Kong,China

3. Alibaba Group,DAMO Academy,Bellevue,US

Publisher

IEEE

Reference24 articles.

1. Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges

2. Robust Load Forecasting Towards Adversarial Attacks via Bayesian Learning

3. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting;Zhou

4. Learning to Rotate

5. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting;Oreshkin

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1. A short survey on Interpretable techniques with Time series data;2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS);2024-02-24

2. Evaluating Model Performance Through a User-Centric Explainable Framework for Probabilistic Load Forecasting Models;2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T);2024-01-18

3. Energy forecasting with robust, flexible, and explainable machine learning algorithms;AI Magazine;2023-10-11

4. A Novel Neural Network Structure for Electricity Load Prediction;2023 3rd Power System and Green Energy Conference (PSGEC);2023-08

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