Comparison of Machine Learning Models for Week-Ahead Load Forecasting in Short-term Power System Planning-Reference-Cited by-同舟云学术

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Comparison of Machine Learning Models for Week-Ahead Load Forecasting in Short-term Power System Planning

Published:2022-10-09 Issue: Volume: Page:
ISSN:
Container-title:2022 North American Power Symposium (NAPS)
language:
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Author:

Olowolaju Joshua¹,Livani Hanif¹

Affiliation:

1. University of Nevada, Reno,Department of Electrical & Biomedical Engineering,Reno,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10012119/10012131/10012181.pdf?arnumber=10012181

Reference25 articles.

1. Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique

2. Electrical Load Forecasting Models for Different Generation Modalities: A Review

3. Tertiary control of microgrid

4. Resilient Distributed Real-Time Demand Response via Population Games

5. Can utilities profit from distributed energy resources?;insights,2017

Cited by 2 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. A GPU-Based Model for Composite Power System Reliability Evaluation and Sensitivity Analysis;2023 IEEE Industry Applications Society Annual Meeting (IAS);2023-10-29

2. Safety Ensured Machine Learning Framework for Energy System Operation in Data Centers;2023 IEEE Kansas Power and Energy Conference (KPEC);2023-04-27

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