Inductive Transfer and Deep Neural Network Learning-Based Cross-Model Method for Short-Term Load Forecasting in Smarts Grids
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
2. Research Computing, Texas A&M University at Qatar, Doha, Qatar
3. Iberdrola Innovation Middle East, Doha, Qatar
Funder
Qatar National Research Fund (a member of Qatar Foundation), and co-funding by IBERDROLA QSTP LLC
“Research Impact Initiative SP5”
Texas A&M University at Qatar
Qatar National Library
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Medicine
Link
http://xplorestaging.ieee.org/ielx7/9349829/10137376/10132308.pdf?arnumber=10132308
Reference41 articles.
1. Big Data Management in Smart Grids: Technologies and Challenges
2. Generalized Hidden-Mapping Ridge Regression, Knowledge-Leveraged Inductive Transfer Learning for Neural Networks, Fuzzy Systems and Kernel Methods
3. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
4. Transfer learning in a transductive setting;rohrbach;Proc Adv Neural Inf Process Syst,2013
5. Energy Predictive Models with Limited Data using Transfer Learning
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