Eventually, linear programming techniques are widely used in the last 40-50 years for power/ energy planning/ use etc.: like

(1) Louveaux, F., Smeers, Y.: Optimal investments for electricity generation, a stochastic model and a test problem. In: Ermoliev, Y., Wets, R.J-B. (eds.) Numerical Techniques for Stochastic Optimization, pp. 33–64. Springer, Berlin (1988)

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(2) Energy Efficiency Improvement of Buildings by Using Linear Programming

Published byTechnische Universität Wien, inIssue 18 • 2019 , pages 38-50

https://journal.ifm.tuwien.ac.at/article/id/138/

https://journal.ifm.tuwien.ac.at/article/138/galley/138/view/

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Energy efficiency is the ability to achieve a desired output or result with the least amount of energy or resources. It can benefit both the environment and the economy, by reducing greenhouse gas emissions, saving costs, and extending the lifespan of devices. For computer engineers, energy efficiency is also a key factor for designing and developing systems that can handle large-scale, complex, and dynamic problems, such as big data, machine learning, and cloud computing.

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You might find this book interesting:

https://link.springer.com/book/10.1007/978-3-030-57442-0

There are different ways to develop a hybrid algorithm. Several methods can be combined to achieve more robust results.

In this paper, there are several interesting examples that might be interesting for you.

Article Hybrid approaches to optimization and machine learning metho...

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Dear Doctor

Go To

Leveraging AI Algorithms for Energy Efficiency: A Smart Energy System Perspective

Jie DONG , Jie GAO , Jinyun YU , Linling KONG , Nian JIANG , Qian WU

Advances in Artificial Intelligence, Big Data and Algorithms G. Grigoras and P. Lorenz (Eds.) © 2023 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/FAIA230792

[Conclusions

The increasing energy demand, national policies, environmental impacts, resource conservation, and grid stability and reliability have led to an inevitable trend of optimizing energy use and promoting sustainability. The use of intelligent energy systems supported by artificial intelligence algorithms has become a feasible solution for optimizing energy use and promoting sustainability. Utilizing artificial intelligence (AI) algorithms in energy systems offers several benefits, but it also comes with its own set of challenges. Data Availability and Quality: AI algorithms require large amounts of highquality data for training and accurate predictions. However, accessing comprehensive and reliable energy data can be challenging, especially in decentralized energy systems or in areas with limited data infrastructure. Model Complexity and Interpretability: AI algorithms, such as deep learning models, can be highly complex and difficult to interpret. This poses challenges in explaining the decision-making process and gaining user trust, particularly in critical energy systems where transparency is important. Cost and Resource Requirements: Implementing AI algorithms in energy systems may require substantial computational resources, data storage, and processing capabilities. The cost of acquiring and maintaining these resources can be a challenge, particularly for smaller energy providers or developing regions. Continuous monitoring, evaluation, and improvement of AI solutions are necessary to ensure their effectiveness and address emerging challenges.]