SoilingEdge: PV Soiling Power Loss Estimation at the Edge Using Surveillance Cameras-Reference-Cited by-同舟云学术

SoilingEdge: PV Soiling Power Loss Estimation at the Edge Using Surveillance Cameras

Published:2024-01 Issue:1 Volume:15 Page:556-566
ISSN:1949-3029
Container-title:IEEE Transactions on Sustainable Energy
language:
Short-container-title:IEEE Trans. Sustain. Energy

Author:

Zhang Wenjie¹^ORCID,Archana Vaidheeswaran²,Gandhi Oktoviano³^ORCID,Rodríguez-Gallegos Carlos D.³^ORCID,Quan Hao⁴^ORCID,Yang Dazhi⁵^ORCID,Tan Chin-Woo⁶,Chung C. Y.¹^ORCID,Srinivasan Dipti²^ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore

3. Solar Energy Research Institute of Singapore, National University of Singapore, Singapore

4. Department of Electrical Engineering, School of Automation, Nanjing University of Science and Technology, Nanjing, China

5. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, China

6. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

Funder

PolyU Start-up Fund

Strategic Hiring Scheme

EMA-PSA

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Renewable Energy, Sustainability and the Environment

Link

http://xplorestaging.ieee.org/ielx7/5165391/10367191/10268055.pdf?arnumber=10268055

Reference49 articles.

1. Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting

2. Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications

3. Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion

4. A Multi-Data Driven Hybrid Learning Method for Weekly Photovoltaic Power Scenario Forecast

5. Exploring Key Weather Factors From Analytical Modeling Toward Improved Solar Power Forecasting

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