Results 101 to 110 of about 4,980 (265)

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source

Graph Neural Network‐Based Prediction of Building Energy Consumption

Energy Science &Engineering, EarlyView.
A graph neural network that encodes a multi‐zone building as a graph accurately predicts hourly cooling and heating loads across three distinct climates, outperforming Random Forest and XGBoost baselines and serving as a fast surrogate to EnergyPlus simulations for scalable building energy management.
Ali Maboudi Reveshti, Jamal Dabbagh, Jhila Nasiri Reveshti, Karim Farajeyan Bonab, Farid Hosseini Mansoub   +4 more
wiley   +1 more source

Assessing the potential for application of machine learning in predicting weather-sensitive waterborne diseases in selected districts of Tanzania

Frontiers in Artificial Intelligence
IntroductionThis study evaluates the potential of machine learning (ML) to predict and manage weather-sensitive waterborne diseases (WSWDs) in selected Tanzanian districts, focusing on environmental health officers' (EHOs) knowledge and perceptions.
Neema Nicodemus Lyimo, Kadeghe Goodluck Fue, Silvia Francis Materu, Ndimile Charles Kilatu, Joseph Philipo Telemala   +4 more
doaj   +1 more source

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Integrated Solar and Wind Energy Systems for Domestic Power Generation and CO2 Emissions Reduction

Energy Science &Engineering, EarlyView.
A well‐structured and sequentially methodical procedure for evaluating the Wind and Photovoltaic Hybrid Energy System‐based technical, environmental, and economic viability. This study investigates the integration of solar photovoltaic(PV) and wind energy systems for domestic electricity generation in the Gaza Strip.
Mohamed Elnaggar, Mai Abuhelwa, Wael A. Salah, Aamir Bashir Taas, Hala J. El‐Khozondar, Yasser F. Nassar, Ahmad Aboul Khail, Alfian Ma'arif, Mohammed J. K. Bashir, Abdurrahman A. Mattar   +9 more
wiley   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

Comparative Analysis of Al2O3/Water–TiO2/Chlorophyll Hybrid Nanofluids in a Photovoltaic–Thermal Solar Water Heating System

Energy Science &Engineering, EarlyView.
TiO2/chlorophyll bio‐hybrid nanofluid achieved the highest PVT performance, increasing thermal efficiency to 38.74% and overall efficiency to 51.35%, compared with 35.96% and 48.45% for Al2O3/water and 27.77% and 40.18% for water. The results demonstrate the potential of bio‐hybrid nanofluids for high‐efficiency solar thermal energy conversion ...
Oluwaseyi Omotayo Alabi, Oluwatoyin Joseph Gbadeyan, Oludolapo Akanni Olanrewaju   +2 more
wiley   +1 more source

Fire and Rescue Service Preparedness for Natural Hazards

Fire and Materials, EarlyView.
ABSTRACT Natural hazards driven by extreme weather events are expected to continue to pose a significant risk to citizens, property and the environment. Based on a series of national and international interviews with the Fire and Rescue Service (FRS), this paper presents an analysis of present practices within the FRS and FRS willingness to employ a ...
Johan Björck, Margaret McNamee
wiley   +1 more source

Green swans and blue skies: Climate change and insolvency risk for financial institutions

International Insolvency Review, EarlyView.
Abstract This lecture in honour of the late Gabriel Moss QC and Ian Fletcher QC examines the challenge of climate‐related financial risk. Prudential regulators and central banks recognize that the systemic nature of climate‐related financial risk makes it an emerging vulnerability relevant to cross‐border insolvency resolution.
Janis Sarra
wiley   +1 more source

Experimental Setup for the Analysis of Vortices

Journal of Applied Fluid Mechanics, 2015
This paper presents a specific experimental setup in which artificial vortices are successfully created and maintained under controlled conditions. The developed vortex simulation chamber is demonstrated to be crucial to the study of vortices and to ...
Sandro Nizetic, Filip Grubisic Cabo, Miro Bugarin   +2 more
doaj