Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review
The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …
and deep learning (DL) architectures is considered one of the most challenging machine …
[HTML][HTML] A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
The efforts to revolutionize electric power generation and produce clean and sustainable
electricity have led to the exploration of renewable energy systems (RES). This form of …
electricity have led to the exploration of renewable energy systems (RES). This form of …
COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications
Power prediction is now a crucial part of contemporary energy management systems, which
is important for the organization and administration of renewable resources. Solar and wind …
is important for the organization and administration of renewable resources. Solar and wind …
Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks
Spatio-temporal wind power forecasting is significant to the stability of electric power
systems. However, the accuracy of power forecasting results is easily impaired by the …
systems. However, the accuracy of power forecasting results is easily impaired by the …
[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 …
Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants
Reservoir level control in hydroelectric power plants has importance for the stability of the
electric power supply over time and can be used for flood control. In this sense, this paper …
electric power supply over time and can be used for flood control. In this sense, this paper …
A novel prediction model for wind power based on improved long short-term memory neural network
J Wang, H Zhu, Y Zhang, F Cheng, C Zhou - Energy, 2023 - Elsevier
Wind power generation technology has attracted worldwide attention. However, its inherent
nonlinearity and uncertainty make itself hard to be accurately predicted. As a result …
nonlinearity and uncertainty make itself hard to be accurately predicted. As a result …
A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction
J Wang, Y Qian, L Zhang, K Wang, H Zhang - Energy Conversion and …, 2024 - Elsevier
Wind power prediction is crucial for successfully integrating large-scale wind energy with the
grid and achieving a carbon-neutral energy mix. However, previous studies encountered …
grid and achieving a carbon-neutral energy mix. However, previous studies encountered …
A review of the applications of artificial intelligence in renewable energy systems: An approach-based study
Recent advancements in data science and artificial intelligence, as well as the development
of clean and sustainable energy sources, have created numerous opportunities for energy …
of clean and sustainable energy sources, have created numerous opportunities for energy …
[HTML][HTML] Evolving long short-term memory neural network for wind speed forecasting
Wind speed forecasting plays a crucial role in reducing the risk of wind power uncertainty,
which is vital for power system planning, scheduling, control, and operation. However, it is …
which is vital for power system planning, scheduling, control, and operation. However, it is …