A synthesis of feasible control methods for floating offshore wind turbine system dynamics
During the past decade, the development of offshore wind energy has transitioned from near
shore with shallow water to offshore middle-depth water regions. Consequently, the energy …
shore with shallow water to offshore middle-depth water regions. Consequently, the energy …
Knowledge structure and research progress in wind power generation (WPG) from 2005 to 2020 using CiteSpace based scientometric analysis
Renewable energy resources have enabled the mitigation of global environmental pollution
and sustainable energy generation. Due to renewability, cleanliness, and vast sustainability …
and sustainable energy generation. Due to renewability, cleanliness, and vast sustainability …
[HTML][HTML] A hybrid extreme learning machine model with lévy flight chaotic whale optimization algorithm for wind speed forecasting
Efficient and accurate prediction of renewable energy sources (RES) is an interminable
challenge in efforts to assure the stable and safe operation of any hybrid energy system due …
challenge in efforts to assure the stable and safe operation of any hybrid energy system due …
A review of applications of artificial intelligent algorithms in wind farms
Wind farms are enormous and complex control systems. It is challenging and valuable to
control and optimize wind farms. Their applications are widely used in various industries …
control and optimize wind farms. Their applications are widely used in various industries …
A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed
Z Tian, S Li, Y Wang - Wind Energy, 2020 - Wiley Online Library
Accurate prediction of short‐term wind speed is of great significance to the operation and
maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable …
maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable …
Short-term wind speed prediction using an extreme learning machine model with error correction
L Wang, X Li, Y Bai - Energy Conversion and Management, 2018 - Elsevier
Wind speed forecasting is an important technology in the wind power field; however,
because of their chaotic nature, predicting wind speeds accurately is difficult. Aims at this …
because of their chaotic nature, predicting wind speeds accurately is difficult. Aims at this …
A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network
Wind speed forecasting (WSF) accuracy is vital for exploiting renewable and environment-
friendly wind energy. Therefore, a hybrid WSF model, namely VIL (VMD-ICEEMDAN-LSTM) …
friendly wind energy. Therefore, a hybrid WSF model, namely VIL (VMD-ICEEMDAN-LSTM) …
[HTML][HTML] Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm
L Wang, Y Guo, M Fan, X Li - Energy Reports, 2022 - Elsevier
Wind speed prediction plays an essential role in wind energy utilization. However, most
existing studies of wind speed forecasting used data from one location to build models and …
existing studies of wind speed forecasting used data from one location to build models and …
Short-term wind speed forecasting using a hybrid model
P Jiang, Y Wang, J Wang - Energy, 2017 - Elsevier
Wind speed forecasting is a crucial issue in the wind power industry. However, the
disadvantage of the existing wind speed forecasting models is that they often ignore similar …
disadvantage of the existing wind speed forecasting models is that they often ignore similar …
Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model
Sustainable utilization of the freely available solar radiation as renewable energy source
requires accurate predictive models to quantitatively evaluate future energy potentials. In …
requires accurate predictive models to quantitatively evaluate future energy potentials. In …