The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction

Malakouti, Seyed matin; Menhaj, Mohammad Bagher; Suratgar, Amir Abolfazl

doi:10.1016/j.clet.2023.100664

Cited by 32 publications

(7 citation statements)

References 27 publications

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“…In this study, Bayesian optimization was chosen due to its effectiveness in handling non-linear and complex search spaces. Unlike traditional grid search or cross-validation methods [72], Bayesian optimization uses probabilistic models to predict the performance of different hyperparameter configurations, guiding the search toward promising regions [73]. This is particularly beneficial in high-dimensional spaces, where an exhaustive search becomes computationally expensive.…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Rastegarpanah,

Asif,

Stolkin

2024

Batteries

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With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

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Section: Hyperparameter Optimizationmentioning

confidence: 99%

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Rastegarpanah,

Asif,

Stolkin

2024

Batteries

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“…Hyperparameter tuning is conducted using randomized search cross-validation (RandomizedSearchCV) [38]. We sample 50 parameter settings (n_iter = 50) to ensure a balanced exploration of hyperparameter configurations.…”

Section: Model Training and Evaluationmentioning

confidence: 99%

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices

Mathotaarachchi,

Hasan,

Mahmood

2024

Information

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Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.

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“…Techniques and tools like grid search, random search, Bayesian optimization, evolutionary algorithms, and others can automatically explore the hyperparameter space to find optimal configurations. Tools that visualize changes (such as Matplotlib, Seaborn, Plotly, Weights & Biases, TensorBoard, Mlflow, Scikit-learn) [39], in the learning process and the impact of hyperparameters can help understand how specific settings affect the model [40].…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Myśliwiec,

Kubit,

Szawara

2024

Materials

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This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints’ maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency.

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The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction

Cited by 32 publications

References 27 publications

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

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