Demand Profiling Oriented Nuisance Tripping Forecasting for Residential Power Network-Reference-Cited by-同舟云学术

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Demand Profiling Oriented Nuisance Tripping Forecasting for Residential Power Network

Published:2023-10-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Energy Conversion Congress and Exposition (ECCE)
language:
Short-container-title:

Author:

Lei Chengwei¹,Tian Weisong²,Liu Zhongzhe³

Affiliation:

1. California State University, Bakersfield,Computer Science Department,Bakersfield,CA,USA

2. Widener University,Department of Electrical Engineering,Chester,PA,USA

3. California State University, Bakersfield,Department of Physics and Engineering,Bakersfield,CA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10361838/10361943/10362888.pdf?arnumber=10362888

Reference20 articles.

1. Modeling and control architecture of source and load management in islanded power systems

2. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models

3. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

4. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques

5. Dynamic Prediction of Building HVAC Energy Consumption by Ensemble Learning Approach

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