Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting
Author:
Affiliation:
1. University of Kassel,Germany
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10068862/10068777/10068974.pdf?arnumber=10068974
Reference22 articles.
1. Representation Learning in Power Time Series Forecasting
2. Usr-mtl: an unsupervised sentence representation learning framework with multi-task learning
3. Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
4. Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning
5. Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems;nivarthi;Organic Computing,2022
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1. Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
2. Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15
3. Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions;Machine Learning and Knowledge Extraction;2023-09-20
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