Machine learning in environmental research: common pitfalls and best practices
Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …
sets and decipher complex relationships between system variables. However, due to the …
Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
Z Zhao, S Yun, L Jia, J Guo, Y Meng, N He, X Li… - … Applications of Artificial …, 2023 - Elsevier
Accurate and reliable short-term forecasting of wind power is vital for balancing energy and
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …
Mean–variance portfolio optimization using machine learning-based stock price prediction
W Chen, H Zhang, MK Mehlawat, L Jia - Applied Soft Computing, 2021 - Elsevier
The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …
markets. Recent developments in machine learning have brought significant opportunities to …
Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
Wind energy increasingly attracts investment from many countries as a clean and renewable
energy source. Since wind energy investment cost is high, the efficiency of a potential wind …
energy source. Since wind energy investment cost is high, the efficiency of a potential wind …
A hybrid attention-based deep learning approach for wind power prediction
Renewable energy, especially wind power, is a practicable and promising solution to
mitigate the existing dilemma associated with climate change. Efficient and accurate …
mitigate the existing dilemma associated with climate change. Efficient and accurate …
Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects
This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …
renewable energy generation using machine learning (ML) and deep learning (DL) …
Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model
D Niu, L Sun, M Yu, K Wang - Energy, 2022 - Elsevier
Accurate and reliable wind power forecasting (WPF) is significant for ensuring power
systems' economic operation and safe dispatching and for reducing the technical and …
systems' economic operation and safe dispatching and for reducing the technical and …
A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
[HTML][HTML] Optimized ensemble model for wind power forecasting using hybrid whale and dipper-throated optimization algorithms
Introduction: Power generated by the wind is a viable renewable energy option. Forecasting
wind power generation is particularly important for easing supply and demand imbalances …
wind power generation is particularly important for easing supply and demand imbalances …