State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques

R Wazirali, E Yaghoubi, MSS Abujazar… - Electric power systems …, 2023 - Elsevier
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …

A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction

J Xiong, T Peng, Z Tao, C Zhang, S Song, MS Nazir - Energy, 2023 - Elsevier
Accurate wind power forecast is critical to the efficient and safe running of power systems. A
hybrid model that combines complementary ensemble empirical mode decomposition …

Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis

C Cakiroglu, S Demir, MH Ozdemir, BL Aylak… - Expert Systems with …, 2024 - Elsevier
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 …

Multivariable space-time correction for wind speed in numerical weather prediction (NWP) based on ConvLSTM and the prediction of probability interval

Y Chen, M Bai, Y Zhang, J Liu, D Yu - Earth Science Informatics, 2023 - Springer
With the advent of the low-carbon era, wind power has become an indispensable energy
source. Accurate day-ahead wind speed forecast is crucial for the power system to absorb …

Wind speed interval prediction based on multidimensional time series of Convolutional Neural Networks

J Wang, Z Li - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Wind power, as an economical and clean energy source, has rapidly infiltrated into modern
power grids. Wind speed prediction is a pivotal technology for wind power integration, which …

A novel selective ensemble system for wind speed forecasting: From a new perspective of multiple predictors for subseries

S Yang, W Yang, X Wang, Y Hao - Energy Conversion and Management, 2023 - Elsevier
Wind speed forecasting is of considerable economic and social significance; however, it
remains challenging. Most state-of-the-art methods attempt to select the optimal predictor for …

Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning

X Cheng, G Ma, Z Wu, H Zu, X Hu - Ndt & E International, 2023 - Elsevier
Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage
in composite materials and the test results need to be interpreted by trained experts. In this …

Deterministic and probabilistic multi-time-scale forecasting of wind speed based on secondary decomposition, DFIGR and a hybrid deep learning method

Z Jiang, J Che, N Li, Q Tan - Expert Systems with Applications, 2023 - Elsevier
Accurate wind speed forecasting is crucial for the stability improvement of wind power grid
connection and the rational dispatch of wind energy resources. Most previous studies have …

[HTML][HTML] Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network …

LP Joseph, RC Deo, D Casillas-Pérez, R Prasad, N Raj… - Applied Energy, 2024 - Elsevier
Wind energy is an environment friendly, low-carbon, and cost-effective renewable energy
source. It is, however, difficult to integrate wind energy into a mixed energy grid due to its …

Quantile deep learning model and multi-objective opposition elite marine predator optimization algorithm for wind speed prediction

J Wang, H Guo, Z Li, A Song, X Niu - Applied Mathematical Modelling, 2023 - Elsevier
Wind speed prediction accuracy is critical for grid connection safety and intelligent wind farm
management. However, most wind speed prediction studies mainly focus on the …