An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems

Bampoulas, Adamantios; Pallonetto, Fabiano; Mangina, Eleni

doi:10.1016/j.apenergy.2022.118947

Cited by 23 publications

(2 citation statements)

References 75 publications

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“…The study project's conclusions show that machine learning models-specifically, the Random Forest model-can significantly improve the precision of predicting the success of SpaceX Falcon 9 rocket launches. This is consistent with earlier research that showed how ensemble learning methods, which can handle high-dimensional data and capture non-linear correlations, could be helpful in complicated prediction scenarios (Bampoulas et al, 2022).…”

Section: Interpretation Of Findingssupporting

confidence: 92%

Mastering the Cosmos: Leveraging Machine Learning to Optimize Spacex Falcon 9 Launch Success Rates

Edafetanure-Ibeh

2024

Preprint

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This study explores the predictive analysis of SpaceX Falcon 9 rocket launch outcomes, utilizing multiple machine learning models to improve prediction success rates. Rocket launch efficiency and dependability have become critical as the commercial space exploration industry proliferates (brown et al., 2023). This exploration period was led by the Falcon 9 rocket, renowned for its revolutionary reusability feature. Predicting results, however, is extremely difficult due to the intricacy of launch variables. This study examines historical launch data through extensive data collecting and organization, concentrating on elements like cargo weight, launch site conditions, and rocket parameters. The research attempts to determine the most accurate predictor of launch success by utilizing many classification models, such as logistic regression, decision trees, and random forests. By identifying significant trends and variables that have a major impact on launch outcomes, the results provide valuable information for improving mission planning and lowering operating risks. This research and its contribution to the corpus of knowledge in predictive analytics and aerospace engineering affect the future of sustainable and affordable space exploration. The results represent a significant advancement in the pursuit of efficient space exploration as they highlight the potential of machine learning to improve the predictability and reliability of commercial space missions.

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Section: Interpretation Of Findingssupporting

confidence: 92%

Mastering the Cosmos: Leveraging Machine Learning to Optimize Spacex Falcon 9 Launch Success Rates

Edafetanure-Ibeh

2024

Preprint

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“…The electrochemical model consists of a set of nonlinear differential equations with numerous parameters, and its use in LIBs products is constrained by the high cost of solving these equations and the difficulty of identifying their parameters. Contrarily, the empirical model requires no formal mechanism description and is obtained using methods for mining vast datasets, such as support vector machines and artificial neural networks [7][8][9][10]. The prediction accuracy of an empirical model is heavily dependent on the training algorithm and training data and it lacks interpretability and needs a lot of data training based on existing datasets before deployment.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Zhang

Deng²,

Zong³

et al. 2022

Batteries

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The validity of the equivalent circuit model (ECM), which is crucial for the development of lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of the findings of parameter identification. In this study, the commonly used first-order RC (1-RC) circuit and second-order RC (2-RC) circuit models were selected for parameter identification. A time series of voltage with different sample intervals were used for function fitting based on the least square method, which were extracted from the hybrid pulse power characteristic (HPPC) test data of a commercial square punch LIB, and the sample intervals were set to be 0.1 s, 0.2 s, 0.5 s, and 1.0 s to evaluate the effect of sample interval on the parameter identification results. When the sample interval is more than 0.5 s, the results reveal that the 2-RC circuit model’s goodness of fit marginally declines, and for some data scenarios, the bias between the fitted terminal voltage curve and test curve increases obviously. With all of the sample intervals under consideration, the 1-RC circuit model’s imitative effect is satisfactory. This work demonstrates that the sample interval of data samples, in addition to the method itself, affects the accuracy and robustness of parameter identification, with the 1-RC circuit model showing larger advantages under low sample frequency compared to the 2-RC circuit model.

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Artificial Intelligence in Building Enclosure Performance Optimization: Frameworks, Methods, and Tools

Kheiri

2024

Artificial Intelligence in Performance‐Driven Design

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An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems

Cited by 23 publications

References 75 publications

Mastering the Cosmos: Leveraging Machine Learning to Optimize Spacex Falcon 9 Launch Success Rates

Mastering the Cosmos: Leveraging Machine Learning to Optimize Spacex Falcon 9 Launch Success Rates

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Artificial Intelligence in Building Enclosure Performance Optimization: Frameworks, Methods, and Tools

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