Photovoltaic power forecasting based on VMD-SSA-Transformer: Multidimensional analysis of dataset length, weather mutation and forecast accuracy DOI
Chao Zhai,

Xinyi He,

Zhixiang Cao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135971 - 135971

Published: April 1, 2025

Language: Английский

A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis DOI
Tabassum Naz Sindhu, Andaç Batur Çolak, Showkat Ahmad Lone

et al.

Tribology International, Journal Year: 2023, Volume and Issue: 192, P. 109231 - 109231

Published: Dec. 27, 2023

Language: Английский

Citations

26
Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems DOI Creative Commons
A. Abou‐Zeid,

Hadeer Gaber Eleraky,

Ahmed Kalas

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 7, 2024

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output of solar panels. Traditional solutions like perturb and observe (P&O) Incremental Conductance (IC) are commonly utilized to follow MPP under various environmental circumstances. However, these algorithms suffer from slow speed low dynamics fast-changing environment conditions. To cope with demerits, data-driven artificial neural network (ANN) algorithm MPPT proposed this paper. By leveraging learning capabilities ANN, PV operating can be adapted dynamic changes irradiation temperature. Consequently, it offers promising environments as well overcoming limitations traditional techniques. In paper, simulations verification experimental validation ANN-MPPT presented. Additionally, analyzed compared methods. The numerical findings indicate that, examined methods, approach achieves highest efficiency at 98.16% shortest time 1.3 s.

Language: Английский

Citations

13
Machine learning-assisted evaluation of PVSOL software using a real-time rooftop PV system: a case study in Kocaeli, Turkey, with a focus on diffuse solar radiation DOI Creative Commons
Ceyda Aksoy Tırmıkçı, Cenk Yavuz, Cem Özkurt

et al.

International Journal of Low-Carbon Technologies, Journal Year: 2025, Volume and Issue: 20, P. 223 - 233

Published: Jan. 1, 2025

Abstract Reducing energy-related CO2 emissions is vital for global climate targets, with Net Zero Energy Buildings (NZEBs) playing a key role. This study evaluates PVSOL software’s accuracy in simulating rooftop photovoltaic (PV) system an NZEB Kocaeli, Turkey. A machine learning model enhanced result reliability using local weather data. The system’s first-year performance ratio was 81.9%, close to the theoretical 84.53%. 435 600 USD investment expected be recovered 11.42 years, while predicts 14.9 years. findings confirm PVSOL’s PV systems, emphasizing their effectiveness reduction and energy transition efforts.

Language: Английский

Citations

1
Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement DOI Creative Commons
Dácil Díaz-Bello, Carlos Vargas‐Salgado, Manuel Alcázar-Ortega

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 27, 2025

Reliable prediction of photovoltaic power generation is key to the efficient management energy systems in response inherent uncertainty renewable sources. Despite advances weather forecasting, accuracy remains a challenge. This study presents novel approach that combines genetic algorithms and dynamic neural network structure refinement optimize prediction. methodology dynamically adjusts parameters during training, including number neurons, transfer functions, weights, biases, minimize root mean square error. Evaluation was performed on twelve representative days using annual, monthly, seasonal data, comparison made with multiple linear regression nonlinear autoregressive models, demonstrating approach's effectiveness. metrics such as error, R-value, percentage error reveal promising accuracy. MATLAB used for modeling, testing, real 4.2 kW PV plant validation. The results indicate significant improvements, errors low 20 W cloudy 175 sunny days. proposed achieves versus target regressions consistency, R values ranging from 0.95824 0.99980, highlighting its efficiency providing reliable predictions generation.

Language: Английский

Citations

1
Comparative assessment of single axis manual solar PV trackers: A case study for agricultural applications DOI Creative Commons

Jad Atallah,

Pierre Rahmé, Jimmy Issa

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100927 - 100927

Published: Feb. 1, 2025

Language: Английский

Citations

1
Machine Learning-Based Evaluation of Solar Photovoltaic Panel Exergy and Efficiency Under Real Climate Conditions DOI Creative Commons
Gökhan Şahin, Wilfried van Sark

Energies, Journal Year: 2025, Volume and Issue: 18(6), P. 1318 - 1318

Published: March 7, 2025

The purpose of this study article is to provide a detailed examination the performance exergy electric panels, efficiency panels and solar under climatic circumstances Utrecht region in Netherlands. explores these terms both their energy efficiency. Additionally, investigates critical factors such as radiation, module internal temperature, air maximum power, Environmental have considerable impact on panel performance; temperature has negative efficiency, whereas an increase radiation leads output. These findings offer significant insights that can be used utilization locations temperate oceanic climate, particularly context conditions region. usefulness linear regression model machine learning was validated by measures R2, RMSE, MAE, MAPE. Furthermore, R2 value 0.94889 found for parameters were utilized. Policy makers, researchers, industry stakeholders who seek successfully utilize face changing may find research important reference.

Language: Английский

Citations

1
Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey DOI Creative Commons
Gökhan Şahin,

Ibrahım Akkus,

Ahmet Koç

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(10), P. e30993 - e30993

Published: May 1, 2024

The determination of the areas where solar power plant will be installed is great importance for performance plant. Solar and hydroelectric energy are most widely used renewable sources in Kars province. Site selection these plants an important factor terms reducing installation cost achieving maximum efficiency during operation. Determining a very complex difficult to analyse spatial decision making problem. In this study, firstly GIS as mapping method obtain locations both Susuz, Arpaçay, Akkaya, city centre, Selim, Digor, Kağızman Sarıkamıș districts province then Taguchi loss function based interval type-2 fuzzy approach applied order more accurate results, results two methods (GIS approach) were also compared. According map obtained, it was determined that total area suitable 78600 km

Language: Английский

Citations

8
Increasing monsoon precipitation extremes in relation to large-scale climatic patterns in Pakistan DOI
Azfar Hussain, Ishtiaq Hussain, Abolfazl Rezaei

et al.

Atmospheric Research, Journal Year: 2024, Volume and Issue: 309, P. 107592 - 107592

Published: July 20, 2024

Language: Английский

Citations

8
An improved microgrid energy management system based on hybrid energy storage system using ANN NARMA-L2 controller DOI
Ouadiâ Chekira, Younes Boujoudar, Hassan El Moussaoui

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 98, P. 113096 - 113096

Published: July 31, 2024

Language: Английский

Citations

6
Recent advances and applications of machine learning in the variable renewable energy sector DOI Creative Commons
Subhajit Chatterjee, Prince Waqas Khan,

Yung-Cheol Byun

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 5044 - 5065

Published: Nov. 8, 2024

Language: Английский

Citations

6