Machine Learning for Solar Array Monitoring, Optimization, and Control

Rao, Sunil; Katoch, Sameeksha; Narayanaswamy, Vivek; Muniraju, Gowtham; Tepedelenlioğlu, Cihan; Spanias, Andreas; Turaga, Pavan; Srinivasan, Devarajan

doi:10.2200/s01027ed1v01y202006pel013

Cited by 26 publications

(20 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abel et al (101) orient movable solar panels via RL in order to maximize their utilization of direct, reflective, and diffuse solar radiation. Rao et al (102) reconfigure the topologies of solar panels to account for shading, using a supervised neural network approach to map irradiance values to potential topologies. Wei et al (103) use RL to control the shaft rotating speed of a wind turbine to maximize power output without requiring wind speed measurements.…”

Section: Maximizing Renewable Power Generationmentioning

confidence: 99%

Machine Learning for Sustainable Energy Systems

Donti

Kolter

2021

Annu. Rev. Environ. Resour.

View full text Add to dashboard Cite

In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

Section: Maximizing Renewable Power Generationmentioning

confidence: 99%

Machine Learning for Sustainable Energy Systems

Donti

Kolter

2021

Annu. Rev. Environ. Resour.

View full text Add to dashboard Cite

show abstract

“…In our previous work [16], [29], we demonstrated the use of a feed-forward fully connected neural network for fault classification on simulated solar fault data generated using Simulink models. In this paper, we propose the use of a concrete dropout and compare the results with uniform dropout [18] neural network architecture for fault classification using PVWatts.…”

Section: Pv Fault Classification Using Custom Neural Networkmentioning

confidence: 99%

Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays

Rao¹,

Muniraju

Tepedelenlioğlu

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Automatic detection of solar array faults reduces maintenance costs and increases efficiency.In this paper, we address the problem of fault detection, localization, and classification in utility-scale photovoltaic (PV) arrays using machine learning methods. More specifically, we develop a series of customized neural networks for detection and classification of solar array faults. We evaluate fault detection and classification using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN). We examine and assess the use of customized neural networks with dropout regularizers. We develop and evaluate neural network pruning strategies and illustrate the trade-off between fault classification model accuracy and algorithm complexity. Our approach promises to elevate the performance and robustness of PV arrays and compares favorably against existing methods.

show abstract

“…The International Research experiences for students (IRES) program also embeds graduate and undergraduate students in sensors and ML research for energy applications [2,37,64,65,66]. The program is collaborative between the UCy KIOS center and the ASU SenSIP center.…”

Section: The Ires Program On Machine Learningmentioning

confidence: 99%

“…The students were initially pre-trained at ASU by taking modules and hands on sessions on ML and signal processing. They have also received training in deep leaning techniques [64,67].…”

Section: The Ires Program On Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Spanias

2020

2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA

Self Cite

View full text Add to dashboard Cite

This paper accompanies the keynote speech at IISA-2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.

show abstract

Machine Learning for Solar Array Monitoring, Optimization, and Control

Cited by 26 publications

References 108 publications

Machine Learning for Sustainable Energy Systems

Machine Learning for Sustainable Energy Systems

Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Contact Info

Product

Resources

About