[HTML][HTML] A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Recent developments in renewable energy have highlighted the need for rational use of
wind energy. Accurate prediction of wind speed and wind power is recognized as an …
wind energy. Accurate prediction of wind speed and wind power is recognized as an …
Deep learning-based multistep ahead wind speed and power generation forecasting using direct method
Long-term effective and accurate wind power potential prediction, especially for wind farms,
facilitates planning for the sustainable development of renewable energy. Accurate wind …
facilitates planning for the sustainable development of renewable energy. Accurate wind …
[HTML][HTML] Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms
As sources of conventional energy are alarmingly being depleted, leveraging renewable
energy sources, especially wind power, has been increasingly important in the electricity …
energy sources, especially wind power, has been increasingly important in the electricity …
[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 …
Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value
JO Ighalo, AG Adeniyi… - Biofuels, Bioproducts and …, 2020 - Wiley Online Library
The higher heating value (HHV) provides information about the quantity of energy contained
in a fuel such as biomass. Correlations and models can be developed to predict biomass …
in a fuel such as biomass. Correlations and models can be developed to predict biomass …
[HTML][HTML] Artificial intelligence in wind speed forecasting: A review
SM Valdivia-Bautista, JA Domínguez-Navarro… - Energies, 2023 - mdpi.com
Wind energy production has had accelerated growth in recent years, reaching an annual
increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power …
increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power …
[HTML][HTML] Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management
J Jamii, M Mansouri, M Trabelsi, MF Mimouni… - Frontiers in Energy …, 2022 - frontiersin.org
The variability of power production from renewable energy sources (RESs) presents serious
challenges in energy management (EM) and power system stability. Power forecasting plays …
challenges in energy management (EM) and power system stability. Power forecasting plays …
Automated extraction of energy systems information from remotely sensed data: A review and analysis
High quality energy systems information is a crucial input to energy systems research,
modeling, and decision-making. Unfortunately, actionable information about energy systems …
modeling, and decision-making. Unfortunately, actionable information about energy systems …
An efficient wind speed prediction method based on a deep neural network without future information leakage
Wind speed has strongly stochastic and fluctuating characteristics that make accurate wind
speed prediction challenging. Traditional hybrid prediction models use future wind speed …
speed prediction challenging. Traditional hybrid prediction models use future wind speed …