Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning …
Nowadays, learning-based modeling methods are utilized to build a precise forecast model
for renewable power sources. Computational Intelligence (CI) techniques have been …
for renewable power sources. Computational Intelligence (CI) techniques have been …
Recent advances in harris hawks optimization: A comparative study and applications
The Harris hawk optimizer is a recent population-based metaheuristics algorithm that
simulates the hunting behavior of hawks. This swarm-based optimizer performs the …
simulates the hunting behavior of hawks. This swarm-based optimizer performs the …
Recent advances in machine learning research for nanofluid-based heat transfer in renewable energy system
Nanofluids have gained significant popularity in the field of sustainable and renewable
energy systems. The heat transfer capacity of the working fluid has a huge impact on the …
energy systems. The heat transfer capacity of the working fluid has a huge impact on 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 …
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 …
Quadratic regression analysis for nonlinear heat source/sink and mathematical Fourier heat law influences on Reiner-Philippoff hybrid nanofluid flow applying …
Background Scientists across the world have tried to explore the effect of non-Newtonian
fluids moving over a symmetric stretchable sheet with the presence of diverse influences …
fluids moving over a symmetric stretchable sheet with the presence of diverse influences …
Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids
Applying machine learning algorithms in the prediction of nanofluids' thermophysical
properties such as density, viscosity, thermal conductivity (TC), and specific heat capacity …
properties such as density, viscosity, thermal conductivity (TC), and specific heat capacity …
Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation
Significant wave height is an average of the largest ocean waves, which are important for
renewable and sustainable energy resource generation. A large significant wave height can …
renewable and sustainable energy resource generation. A large significant wave height can …
Computational methods to simulate molten salt thermophysical properties
T Porter, MM Vaka, P Steenblik… - Communications …, 2022 - nature.com
Molten salts are important thermal conductors used in molten salt reactors and solar
applications. To use molten salts safely, accurate knowledge of their thermophysical …
applications. To use molten salts safely, accurate knowledge of their thermophysical …