Hybridization of hybrid structures for time series forecasting: A review
Achieving the desired accuracy in time series forecasting has become a binding domain,
and develo** a forecasting framework with a high degree of accuracy is one of the most …
and develo** a forecasting framework with a high degree of accuracy is one of the most …
Deep learning in electrical utility industry: A comprehensive review of a decade of research
Smart-grid (SG) is a new revolution in the electrical utility industry (EUI) over the past
decade. With each moving day, some new advanced technologies are coming into the …
decade. With each moving day, some new advanced technologies are coming into the …
HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting
The forecasting and estimation of wind power is a challenging problem in renewable energy
generation due to the high volatility of wind power resources, inevitable intermittency, and …
generation due to the high volatility of wind power resources, inevitable intermittency, and …
Very short-term forecasting of wind power generation using hybrid deep learning model
Accurate forecasting of wind power generation plays a key role in improving the operation
and management of a power system network and thereby its reliability and security …
and management of a power system network and thereby its reliability and security …
[HTML][HTML] Wind power prediction based on EEMD-Tent-SSA-LS-SVM
Z Li, X Luo, M Liu, X Cao, S Du, H Sun - Energy Reports, 2022 - Elsevier
To solve the wind power prediction problem, the Improved Sparrow Search Algorithm-Least
Squares Support Vector Machine (ISSA-LS-SVM) prediction model based on chaotic …
Squares Support Vector Machine (ISSA-LS-SVM) prediction model based on chaotic …
[HTML][HTML] Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition
Y Bai, MD Liu, L Ding, YJ Ma - Applied Energy, 2021 - Elsevier
Due to the strong randomness of wind speed, wind power generation is difficult to integrate
into the grid. It is very important to predict wind speed reliably and accurately so that wind …
into the grid. It is very important to predict wind speed reliably and accurately so that wind …
[HTML][HTML] Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems
Large scale integration of renewable energy system with classical electrical power
generation system requires a precise balance to maintain and optimize the supply–demand …
generation system requires a precise balance to maintain and optimize the supply–demand …
A CNN encoder decoder LSTM model for sustainable wind power predictive analytics
Wind Power (WP) proliferates as one of the significant sustainable energies available in the
form of temporal intervals. WP exists as a natural energy generating resource that depends …
form of temporal intervals. WP exists as a natural energy generating resource that depends …
Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting
Power supply from renewable energy is an important part of modern power grids. Robust
methods for predicting production are required to balance production and demand to avoid …
methods for predicting production are required to balance production and demand to avoid …
Hybrid forecasting models for wind-PV systems in diverse geographical locations: performance and power potential analysis
In order to combat the global warming, much stress is put on integrating non-conventional
energy resources, such as wind power plants and solar energy systems, into standard …
energy resources, such as wind power plants and solar energy systems, into standard …