Weekly Load Demand Forecasting using Supervised Deep Learning Techniques: A Case Study of Suranaree University of Technology-Reference-Cited by-同舟云学术

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Weekly Load Demand Forecasting using Supervised Deep Learning Techniques: A Case Study of Suranaree University of Technology

Published:2023-03-08 Issue: Volume: Page:
ISSN:
Container-title:2023 International Electrical Engineering Congress (iEECON)
language:
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Author:

Sarapan Waranyu¹,Khotsriwong Nonthawat¹,Boonrakchat Nonthakorn¹,Boonraksa Promphak²,Boonraksa Terapong³,Marungsri Boonruang¹

Affiliation:

1. School of Electrical Engineering, Institute of Engineering Suranaree University of Technology,Nakhon Ratchasima,Thailand

2. School of Electrical Engineering, Rajamangala University of Technology Suvarnabhumi,Faculty of Engineering and Architecture,Nonthaburi,Thailand

3. School of Electrical Engineering, Rajamangala University of Technology Rattanakosin,Faculty of Engineering,Salaya Nakhon Pathom,Thailand

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10126135/10126143/10126612.pdf?arnumber=10126612

Reference11 articles.

1. Long Short-Term Memory;beysolow;Introduction to deep learning using R A step-by-step guide to learning and implementing deep learning models using R,2017

2. Recurrent Neural Networks;beysolow;Introduction to deep learning using R A step-by-step guide to learning and implementing deep learning models using R,2017

3. Deep Learning-Based Forecasting Approach in Smart Grids With Microclustering and Bidirectional LSTM Network

4. Short-term Load Forecasting Model of GRU Network Based on Deep Learning Framework;gao;2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2),0

5. Short-Term Load Forecasts Using LSTM Networks

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