[HTML][HTML] A hybrid extreme learning machine model with lévy flight chaotic whale optimization algorithm for wind speed forecasting
Efficient and accurate prediction of renewable energy sources (RES) is an interminable
challenge in efforts to assure the stable and safe operation of any hybrid energy system due …
challenge in efforts to assure the stable and safe operation of any hybrid energy system due …
LSTM based 24 hours ahead forecasting of solar PV system for standalone household system
SU Sabareesh, KSN Aravind, KB Chowdary… - Procedia Computer …, 2023 - Elsevier
A rapid expansion in the technology of solar PV energy in recent years has paved the way
for PV market to grow resulting in a cost reduction in material. Hence, technological …
for PV market to grow resulting in a cost reduction in material. Hence, technological …
[HTML][HTML] Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms
Wind power plays a critical role in supporting Ethiopia's electricity generation, particularly
during dry seasons when hydropower availability diminishes. This contribution becomes …
during dry seasons when hydropower availability diminishes. This contribution becomes …
[HTML][HTML] Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery
J Ouyang, H Lin, Y Hong - Energies, 2024 - mdpi.com
The state of charge (SOC) is a core parameter in the battery management system for LMFP
batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy …
batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy …
An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems
The current generation portfolio is obligated to incorporate zero-emissions energy sources,
predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of …
predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of …
Heteroscedasticity effects as component to future stock market predictions using RNN-based models
Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock
volatility forecasting provides business insight into the stock market, making it valuable …
volatility forecasting provides business insight into the stock market, making it valuable …
Predicting small molecules solubility on endpoint devices using deep ensemble neural networks
Aqueous solubility is a valuable yet challenging property to predict. Computing solubility
using first-principles methods requires accounting for the competing effects of entropy and …
using first-principles methods requires accounting for the competing effects of entropy and …
Comparative Analysis of Machine Learning Algorithms to Predict Solar Irradiance
A Balan, T Ramanathan - 2022 International Conference on …, 2022 - ieeexplore.ieee.org
In recent times, there has been a rapid shift from non-renewable to renewable sources of
energy. Recently there has been a lot of development in photovoltaic systems that use solar …
energy. Recently there has been a lot of development in photovoltaic systems that use solar …
Support Vector Machine based Short Term Solar Power Prediction
U Kumar, S Syama - 2022 6th International Conference on …, 2022 - ieeexplore.ieee.org
As solar energy is one of the most abundant sources of renewable energy, there has been
substantial fluctuations in the energy supply from PV systems when used with power grids …
substantial fluctuations in the energy supply from PV systems when used with power grids …
Hybrid Deep Learning Model to Forecast Crude Oil Price
GR Govind, AS Babu - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Crude Oil is one of the world's most essential commodities, and its price greatly influences
the global economy. Both the public and corporate sectors place high importance on …
the global economy. Both the public and corporate sectors place high importance on …