Deep learning models for solar irradiance forecasting: A comprehensive review
The growing human population in this modern society hugely depends on the energy to
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
Machine learning technology in biodiesel research: A review
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
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 …
Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects
With the rapid advancement of technology in the energy sector and the demand for
sustainable energy practices, the world is aiming at fostering the hydrogen economy and …
sustainable energy practices, the world is aiming at fostering the hydrogen economy and …
Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale …
This work examined the thermal performance of a small-scale solar organic Rankine cycle
system, in which a flat plate solar collector was employed to supply heat to the organic …
system, in which a flat plate solar collector was employed to supply heat to the organic …
Using Bayesian optimization and ensemble boosted regression trees for optimizing thermal performance of solar flat plate collector under thermosyphon condition …
The thermal performance of a flat plate solar collector operating under thermosyphon
conditions using MWCNT+ Fe 3 O 4/Water hybrid nanofluids was investigated in this study …
conditions using MWCNT+ Fe 3 O 4/Water hybrid nanofluids was investigated in this study …
Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks
This paper presents a comprehensive review of machine learning (ML) based approaches,
especially artificial neural networks (ANNs) in time series data prediction problems …
especially artificial neural networks (ANNs) in time series data prediction problems …
[HTML][HTML] Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer …
Cloud dynamics are the main factor influencing the intermittent variability of short-term solar
irradiance, and therefore affect the solar farm output. Sky images have been widely used for …
irradiance, and therefore affect the solar farm output. Sky images have been widely used for …
Pyrolysis characteristics, artificial neural network modeling and environmental impact of coal gangue and biomass by TG-FTIR
H Bi, C Wang, Q Lin, X Jiang, C Jiang, L Bao - Science of the Total …, 2021 - Elsevier
The harm done to the environment by coal gangue was very serious, and it is urgent to
adopt effective methods to dispose of coal gangue in order to prevent further environmental …
adopt effective methods to dispose of coal gangue in order to prevent further environmental …
[HTML][HTML] Trends and gaps in photovoltaic power forecasting with machine learning
The share of solar energy in the electricity mix increases year after year. Knowing the
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …