Short-term district power load self-prediction based on improved XGBoost model

Cao, Wangbin; Liu, Yanping; Mei, Huawei; Shang, Huifang; Yu, Yang

doi:10.1016/j.engappai.2023.106826

Cited by 26 publications

(3 citation statements)

References 24 publications

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“…It is very popular among researchers since it can handle very non-linear datasets and demonstrate good performance in most of cases [85,86]. In fact, it is an ensemble ML algorithm that makes an estimation by the combination of the results of multiple weak learners like the decision tree method [87]. Figure 3 demonstrates how the XGBoost combines the predicted results of subtrees using a weighted average.…”

Section: Xgboostmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

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The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids' superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modelling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN's superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithms models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable to consider many effective parameters into account with very higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF, but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

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Section: Xgboostmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

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“…PSO is a collective intelligence algorithm fashioned upon the emulation of foraging patterns within bird flocks [18][19]. It has applications in system identification and control.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Designing and modeling of a new MSMA vibration energy transducer

Luping,

Jing,

Yunhong

2024

J. vibroeng.

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The conversion of mechanical vibrational energy into electrical energy to power wireless electronic devices using the smart material magnetic shape memory alloy (MSMA) has garnered substantial attention. This paper presents a vibration energy transducer founded on the inverse effect of MSMA, elucidating the principle of power generation. The variations in martensite and magnetic domain characteristics within MSMA were analyzed, and a constitutive model was established for the MSMA vibration energy transducer, integrating the thermodynamic theory of the Gibbs free energy function. Although this model performs well in predicting experimental outcomes, it falls short in capturing all features of the experimental data. To comprehensively encompass these features, the PSO-XGBoost machine learning approach was introduced to train the experimental data by incorporating factors such as stress, magnetic field, and induced voltage. An experimental prototype of the MSMA vibration energy transducer is fabricated, and the predictions of both models are compared with the collected experimental data, validating the accuracy of the model and indicating the enhanced effectiveness of machine learning methods in prediction. This research not only validates the correctness of the models but also emphasizes the potential for more precise predictions using machine learning methods, thereby establishing a robust foundation for the thorough study and broader application of MSMA vibration energy transducers.

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“…XGBoost stands for eXtremely Gradient Boosting is a kind of ensemble technique that is well-known for its high accuracy of prediction and fast training process. Like any other ensemble ML technique, XGBoost uses the results of several decision trees (also referred to as boosting rounds) to make a more concise estimation [41]. This method which was first proposed by Chen et al [42] has the capability of handling both classification and regression problems.…”

Section: Xgboostmentioning

confidence: 99%

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

Alipour Bonab,

Xing,

Russo

et al. 2024

Supercond. Sci. Technol.

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The advent of superconducting bulks, because of their compactness and performance, offers new perspectives and opportunities in many applications and sectors, such as magnetic field shielding, motors/generators, NMR/MRI, magnetic bearings, flywheel energy storage, Maglev trains, among others. The investigation and characterization of bulks typically relies on time-consuming and expensive experimental campaigns; hence the development of effective surrogate models would considerably speed up the research progress around them. In this study, we have first produced an experimental dataset with the levitation and lateral forces between different MgB2 bulks and one permanent magnet under different operating conditions. Next, we have exploited the dataset to develop surrogate models based on Artificial Intelligence (AI) techniques, namely Extremely Gradient Boosting (XGBoost), Support Vector Machine Regressor (SVR), and Kernel Ridge Regression (KRR). After the tuning of the hyperparameters of the AI models, the results demonstrated that SVR is the superior technique and can predict levitation and lateral forces with a worst-case accuracy scenario 99.86% in terms of goodness of fit to experimental data. Moreover, the response time of these models for prediction of new datapoints is ultra-fast.

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Short-term district power load self-prediction based on improved XGBoost model

Cited by 26 publications

References 24 publications

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Designing and modeling of a new MSMA vibration energy transducer

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

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Short-term district power load self-prediction based on improved XGBoost model

Cited by 26 publications

References 24 publications

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Designing and modeling of a new MSMA vibration energy transducer

Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques

Contact Info

Product

Resources

About

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques