Results 51 to 60 of about 4,088 (233)

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

Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing

JISA (Jurnal Informatika dan Sains), 2023
To reduce the discrepancy between the results of the expected data and the actual data, prediction is a procedure that is calculated systematically based on owned historical and present information.
Alfin Syarifuddin Syahab, Arief Hermawan, Donny Avianto   +2 more
doaj   +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

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

Evaluation of WRF parameterizations for global horizontal irradiation forecasts: A study for Turkey

Atmósfera, 2019
This study deals with the evaluation of the Weather Research and Forecast (WRF) model’s parameterization schemes when used for global horizontal irradiation (GHI) forecasts, to assess the feasibility of solar energy utilization. The study area is the southeastern Anatolia (SEA) region of Turkey, with favorable weather conditions for solar energy ...
Incecik S, Sakarya S, Tilev-Tanriover S, Kahraman A, Aksoy B, Caliskan E, Topcu S, Kahya C, Odman MT   +8 more
openaire   +4 more sources

Techno‐Economic and Quasi‐Dynamic Load Flow Analysis of a Hybrid Renewable Microgrid for Coastal Industrial Loads

Energy Science &Engineering, EarlyView.
A hybrid PV–wind grid‐connected microgrid for Mongla Port is optimized using HOMER Pro and validated via DIgSILENT quasi‐dynamic load flow. The system achieves a low cost of energy (0.0341 USD/kWh), 70.9% renewable penetration, improved voltage stability, and significant emission reduction, demonstrating a reliable and cost‐effective solution for ...
Md. Behesty Hasan Sagor, Md Abdus Samad Kamal, Md. Feroz Ali, Md. Mahedi Hasan, Fahad Saleh Al‐Ismail, Md Shafiul Alam, Mohammad Ali   +6 more
wiley   +1 more source

Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks

Journal of Modern Power Systems and Clean Energy, 2018
In order to develop predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems, accurate and reliable photovoltaic (PV) power forecasts are required.
Saad Parvaiz DURRANI, Stefan BALLUFF, Lukas WURZER, Stefan KRAUTER   +3 more
doaj   +1 more source

Geographical distribution of the angle of incidence uncertainty on the measurement of global horizontal irradiance

Measurement: Sensors, 2022
One of the main contributing factors to uncertainty for ground-based global irradiance measurements is the angular response of the pyranometer. Since it depends on the angle of incidence, the measurement uncertainty will depend on the time of day and ...
Miguel C. Brito, Rodrigo Amaro e Silva, Mário Pó   +2 more
doaj   +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

Microbiome‐mediated plant‐soil feedbacks as a tool for resilient agriculture

iMetaOmics, EarlyView.
Translating plant‐soil feedbacks (PSFs) into reliable agricultural management requires shifting from input‐dependent practices toward endogenous, process‐driven crop resilience. To bridge this gap, we propose a predictive framework centered on three strategic interventions: engineering the crop holobiont, diversifying the soil habitat, and deploying ...
Muhammad Asif, Hazrat Bilal, Min Xiao, Lei Dong, Wen‐Jun Li   +4 more
wiley   +1 more source