Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach
Due to the energy crisis and environmental concerns, wind power has seen a considerable
increase in use over the past 10 years as a source of renewable energy. Since wind is …
increase in use over the past 10 years as a source of renewable energy. Since wind is …
An integrated deep neural network framework for predicting the wake flow in the wind field
Ultra-short-term wake flow prediction is crucial for wind resource assessment and wind farm
operation control. To improve the power generation efficiency and stable operation level of …
operation control. To improve the power generation efficiency and stable operation level of …
[HTML][HTML] Solar and Wind Data Recognition: Fourier Regression for Robust Recovery
AF Al-Aboosi, AJ Muñoz Vazquez… - Big Data and Cognitive …, 2024 - mdpi.com
Accurate prediction of renewable energy output is essential for integrating sustainable
energy sources into the grid, facilitating a transition towards a more resilient energy …
energy sources into the grid, facilitating a transition towards a more resilient energy …
[HTML][HTML] Wind power deviation charge reduction using long short term memory network
High penetration of variable generation like wind in modern power systems results in
frequent load-generation imbalances. Additional spinning reserves and balancing services …
frequent load-generation imbalances. Additional spinning reserves and balancing services …
Wind Power Forecasting With LSTM and Comparison With Different Machine Learning Algorithms: A Case Study of Southwestern Turkey
MA Yelgeç, O Bingöl - Electric Power Components and Systems, 2024 - Taylor & Francis
Wind is a renewable energy resource but its intermittent nature poses some issues for
transmission system operators, wind farms, and the electricity market. To address these …
transmission system operators, wind farms, and the electricity market. To address these …
Fuzzy reliability evaluation and machine learning-based fault prediction of wind turbines
The swift growth of the wind power industry necessitates comprehensive evaluation and
efficient fault prediction of wind turbines. Given the challenges of integration and …
efficient fault prediction of wind turbines. Given the challenges of integration and …
Robust Anomaly Detection for Offshore Wind Turbines: A Comparative Analysis of AESE Algorithm and Existing Techniques in SCADA Systems
F Chenglin, JG Hur, CG Lim - Proceedings of the 2024 8th International …, 2024 - dl.acm.org
Offshore wind turbines (OWTs) installed far from land have historically faced significant
maintenance costs and loss of power generation resources due to system failures. As the …
maintenance costs and loss of power generation resources due to system failures. As the …
Exploratory Data Analysis and Energy Predictions With Advanced AI and ML Techniques
T Santhoshkumar, S Vanila - AI Approaches to Smart and …, 2024 - igi-global.com
Solar and wind energy forecasting is vital for efficient energy management and sustainable
power grid operations. This chapter explores machine learning (ML) algorithms for solar and …
power grid operations. This chapter explores machine learning (ML) algorithms for solar and …
[HTML][HTML] Significant Wave Height Forecasting using Long-Short Term Memory (LSTM) in Seribu Island Waters
Wind waves are natural phenomena primarily generated by the wind. Information about
wave height and period is highly crucial in various marine fields such as coastal …
wave height and period is highly crucial in various marine fields such as coastal …
[PDF][PDF] Optimizing Prediction Error for Time-dependent Solar Radiation Modeling
D McDowell, A Epiney, R Flanagan, M Deinert - 2024 - inldigitallibrary.inl.gov
The variability of renewable energy generators is expected to place increasing stress on the
existing electrical grids in the United States [1]. Thus, accurate forecasting of renewable …
existing electrical grids in the United States [1]. Thus, accurate forecasting of renewable …