Predicting Cd Accumulation in Crops and Identifying Nonlinear Effects of Multiple Environmental Factors Based on Machine Learning Models DOI

Xiaosong Lu,

Xuzhi Li,

Li Sun

et al.

Published: Jan. 1, 2024

Language: Английский

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models DOI

Xiaosong Lu,

Li Sun,

Ya Zhang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175787 - 175787

Published: Aug. 24, 2024

Language: Английский

Citations

8
Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting DOI Open Access

Jinyeong Oh,

Dayeong So,

Jaehyeok Jo

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(9), P. 1659 - 1659

Published: April 25, 2024

Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability effectively learn unstable environmental variables and complex interactions. However, NNs are limited practical industrial application the energy sector because optimization of model structure or hyperparameters is a time-consuming task. This paper proposes two-stage NN method for robust PV forecasting. First, dataset divided into training test sets. In set, several models with different numbers hidden layers constructed, Optuna applied select optimal hyperparameter values each model. Next, optimized layer used generate estimation prediction fivefold cross-validation on sets, respectively. Finally, random forest values, from set as input predict final power. As result experiments Incheon area, proposed not only easy but also outperforms models. case point, New-Incheon Sonae dataset—one three various locations—the achieved an average mean absolute error (MAE) 149.53 kW root squared (RMSE) 202.00 kW. These figures significantly outperform benchmarks attention mechanism-based deep learning models, scores 169.87 MAE 232.55 RMSE, signaling advance that expected make significant contribution South Korea’s industry.

Language: Английский

Citations

6
Digital twin technology and artificial intelligence in energy transition: A comprehensive systematic review of applications DOI

Abdelali Abdessadak,

Hicham Ghennioui, Nadège Thirion‐Moreau

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 5196 - 5218

Published: May 3, 2025

Language: Английский

Citations

0
Predicting Cd Accumulation in Crops and Identifying Nonlinear Effects of Multiple Environmental Factors Based on Machine Learning Models DOI

Xiaosong Lu,

Xuzhi Li,

Li Sun

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0