Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient...
Saved in:
Main Authors: | Li Sun, Fengqi You |
---|---|
Format: | article |
Language: | EN |
Published: |
Elsevier
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/b65f85a5c83243b6b2c87839a2f619e0 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Innovation and Development Strategies of China’s New-Generation Smart Vehicles Based on 4S Integration
by: Liu Zongwei, et al.
Published: (2021) -
Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques
by: Ning Zhao, et al.
Published: (2021) -
Smart City Decision Making System Based on Event-driven Platform
by: Saric Andrej, et al.
Published: (2021) -
Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
by: Mahdi Bahaghighat, et al.
Published: (2021) -
Uncertainty modeling of household appliance loads for smart energy management
by: Yu Wang, et al.
Published: (2022)