State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …
operation because more precise forecasts mean reduced risk and improved stability and …
A review of wind speed and wind power forecasting with deep neural networks
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …
has attracted increasing attention. However, intermittent electricity generation resulting from …
[HTML][HTML] A review of green artificial intelligence: Towards a more sustainable future
Green artificial intelligence (AI) is more environmentally friendly and inclusive than
conventional AI, as it not only produces accurate results without increasing the …
conventional AI, as it not only produces accurate results without increasing the …
Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review
M Kaveh, MS Mesgari - Neural Processing Letters, 2023 - Springer
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 …
Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
Load forecasting techniques for power system: Research challenges and survey
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …
think tank of power sectors should forecast the future need of electricity with large accuracy …
A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
Reliable and accurate wind speed forecasting (WSF) is fundamental for efficient exploitation
of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant …
of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant …
A review of very short-term wind and solar power forecasting
R Tawn, J Browell - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
Installed capacities of wind and solar power have grown rapidly over recent years, and the
pool of literature on very short-term (minutes-to hours-ahead) wind and solar forecasting has …
pool of literature on very short-term (minutes-to hours-ahead) wind and solar forecasting has …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
Near real-time wind speed forecast model with bidirectional LSTM networks
Wind is an important source of renewable energy, often used to provide clean electricity to
remote areas. For optimal extraction of this energy source, there is a need for an accurate …
remote areas. For optimal extraction of this energy source, there is a need for an accurate …