[HTML][HTML] FOX-TSA hybrid algorithm: Advancing for superior predictive accuracy in tourism-driven multi-layer perceptron models
Nature-inspired optimization models have received a great deal of interest due to the
performance of these algorithms in solving resourceful and authentic problems. However …
performance of these algorithms in solving resourceful and authentic problems. However …
[HTML][HTML] A Comparative Study of Machine Learning Models for Predicting Meteorological Data in Agricultural Applications
This study aims to address the challenges of climate change, which has led to extreme
temperature events and reduced rainfall, using Internet of Things (IoT) technologies …
temperature events and reduced rainfall, using Internet of Things (IoT) technologies …
Enhancing solar forecasting accuracy with sequential deep artificial neural network and hybrid random forest and gradient boosting models across varied terrains
Effective solar energy utilization demands improvements in forecasting due to the
unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two …
unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two …
Predicting photovoltaic greenhouse irradiance at low-latitudes of plateau based on ultra-short-term time series
Y Zhu, G Li, Y Jiang, M Li, Y Wang, Y Zhang, Y Liu… - Renewable Energy, 2025 - Elsevier
Accurate and reliable ultra-short-term prediction of solar irradiance in photovoltaic (PV)
greenhouses at low-latitude plateau is essential to precisely control electricity consumption …
greenhouses at low-latitude plateau is essential to precisely control electricity consumption …
Enhanced accuracy in solar irradiance forecasting through machine learning stack-based ensemble approach
Accurate solar irradiance (SI) prediction is vital for optimizing solar photovoltaic systems.
This study addresses shortcomings in existing forecasting methods by exploring advanced …
This study addresses shortcomings in existing forecasting methods by exploring advanced …
Solar irradiation forecast enhancement using clustering based CNN-BiLSTM-attention hybrid architecture with PSO
M Chiranjeevi, A Madyastha, AK Maurya… - … Journal of Ambient …, 2024 - Taylor & Francis
Accurate solar irradiation forecasting is essential for optimising solar energy use. This paper
presents a novel forecasting approach: the 'Clustering-based CNN-BiLSTM-Attention Hybrid …
presents a novel forecasting approach: the 'Clustering-based CNN-BiLSTM-Attention Hybrid …
[HTML][HTML] Short-Term Irradiance Prediction Based on Transformer with Inverted Functional Area Structure
Z Zhuang, H Wang, C Yu - Mathematics, 2024 - mdpi.com
Solar irradiance prediction is a crucial component in the application of photovoltaic power
generation, playing a vital role in optimizing energy production, managing energy storage …
generation, playing a vital role in optimizing energy production, managing energy storage …
An Ensemble Supervised Machine Learning Model for Solar Irradiance Prediction Using Tree-Based Learners
H Shankar, S Namasudra, M Kumar… - … Symposium on Sustainable …, 2024 - Springer
To facilitate renewable energy to be more deeply integrated into the current power system's
controls, an accurate solar energy prediction is crucial. Data-driven algorithms offer a …
controls, an accurate solar energy prediction is crucial. Data-driven algorithms offer a …
[PDF][PDF] Systems and Soft Computing
Nature-inspired optimization models have received a great deal of interest due to the
performance of these algorithms in solving resourceful and authentic problems. However …
performance of these algorithms in solving resourceful and authentic problems. However …