Short-Term Load Forecasting in DSO Substation Networks with Dimensionality Reduction Techniques-Reference-Cited by-同舟云学术

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Short-Term Load Forecasting in DSO Substation Networks with Dimensionality Reduction Techniques

Published:2022-06-28 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
language:
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Author:

Grimaccia Francesco¹,Mussetta Marco¹,Niccolai Alessandro¹,Veeramsetty Venkataramana²,Chandra Dongari Rakesh³

Affiliation:

1. Politecnico di Milano,Department of Energy,Milan,Italy

2. Center for AI and Deep Learning, SR University,Dept. of EEE Warangal,India

3. Kakatiya Institute of Technology and Science (KITS),Department of Electrical and Electronics Engineering,Warangal,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9854509/9854408/09854749.pdf?arnumber=9854749

Reference24 articles.

1. Machine-Learning based methods in short-term load forecasting

2. Convolutional and recurrent neural network based model for short-term load forecasting

3. Convolutional residual network to short-term load forecasting

4. Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

5. Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Active Power Load and Electrical Energy Price Datasets for Load and Price Forecasting;Green Energy and Technology;2024

2. Power Quality Disturbances Data Dimensionality Reduction Using Autoencoder;Green Energy and Technology;2024

3. Active Power Load Data Dimensionality Reduction Using Autoencoder;Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques;2023

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