Sep 2, 2021 · The four machine learning regression models—linear regression, k-nearest neighbors, random forest, and decision tree—were developed using the scikit-learn library in Python and validated with experimental data.

Apr 1, 2023 · In this review article, we summarise extensive lists of literature on BTMs employing ML models and identify the current state-of-the-art research, which is expected to serve as a much-needed guideline and reference for future design optimisation.

Sep 1, 2023 · In order to resolve some of the issues associated with finding a balance between thermal comfort and energy savings, machine learning has received an increasing level of attention. Machine learning is a set of tools designed to extract information from data, reshape data, learn complex patterns, and make informed decisions.

Aug 23, 2021 · The four machine learning regression models—linear regression, k-nearest neighbors, random forest, and decision tree—were developed using the scikit-learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R 2 metric.

In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales.

Jul 28, 2017 · In this paper we present a framework for creating a lightweight thermal prediction system suitable for run-time management decisions. We pursue two avenues to explore optimized lightweight thermal predictors. First, we use feature selection algorithms to improve the performance of previously designed machine learning methods.

Nov 1, 2023 · In a comprehensive study, various machine learning methods and neural networks used in battery temperature prediction and thermal management are analyzed and discussed along with its various training algorithms.

Nov 18, 2021 · The data treatment was realised by using the programming language Python, especially the ML module called Scikit-learn [Citation 38], which enables the user to train and test all kind of ML algorithms, among many other functionalities. Additional information about this Python module can be found in Ref. [Citation 39].

Aug 23, 2021 · In a comprehensive study, various machine learning methods and neural networks used in battery temperature prediction and thermal management are analyzed and discussed along with its...

In this report we will investigate the use of Python to support state-of-charge estimation, diagnostic, and system optimization challenges. Through using Python, we improve the battery performance, reliability, and cost efficiency key findings.