Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)

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Transfer Learning in Real-Time Optimization

Authors
Prudhvi Naayini1, *, Ugandhar Dasi2
1University of Colorado Boulder, Boulder, CO, USA
2Independent Researcher, Atlanta, USA
*Corresponding author. Email: prudhvi.Naayini@colorado.edu
Corresponding Author
Prudhvi Naayini
Available Online 17 July 2025.
DOI
10.2991/978-94-6463-787-8_46How to use a DOI?
Keywords
Real-time optimization (RTO); Transfer learning (TL); Ma chine learning; Adaptive control; Process optimization; Domain adaptation
Abstract

Real-time optimization (RTO) is a cornerstone of modern engineering systems, enabling dynamic adjustments to operational conditions for enhanced efficiency, reduced costs, and improved system performance. However, the inherent challenges of RTO, including limited data availability and high computational demands, necessitate innovative solutions. Transfer learning (TL), a powerful subset of machine learning, offers transformative potential by leveraging knowledge from related tasks to accelerate adaptation and improve decision-making. This article delves into the integration of TL into RTO, highlighting methodologies such as feature-based and model-based approaches, applications across diverse domains like energy systems and healthcare, and addressing key challenges including domain divergence and real-time constraints. By exploring these aspects, this work underscores the potential of TL to significantly advance the adaptability and performance of real-time optimization systems across various industries.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
Series
Advances in Intelligent Systems Research
Publication Date
17 July 2025
ISBN
978-94-6463-787-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-787-8_46How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Prudhvi Naayini
AU  - Ugandhar Dasi
PY  - 2025
DA  - 2025/07/17
TI  - Transfer Learning in Real-Time Optimization
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 593
EP  - 606
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_46
DO  - 10.2991/978-94-6463-787-8_46
ID  - Naayini2025
ER  -