Effective Spline-GMPPT Approach Performance for Photovoltaic Systems under Uniform Irradiance and Partial Shading Conditions DOI
Ahmed Saidi, Touhami Abdelouahed, Abdelghani Draoui

et al.

Published: Dec. 12, 2023

In the literature, there are several criteria techniques classified to achieve point of maximum power tracking (MPPT) in photovoltaic (PV) systems, such as accuracy, speed, and simplicity. These often trade-offs; this case, higher accuracy generally achieved at expense two other criteria: speed simplicity.This contribution proposes a new technique based on Spline-Global-MPPT approach provide its reliability multiple an accurate, fast, simple find MPP PV generation systems under cases, uniform irradiance partial shading conditions (PSCs), total distorted characteristics string. A cubic spline interpolation has proposed method, which defines from few points approximate function.To localize GMPP standard (uniform) conditions, huge number interpolation-based approaches literature. Unfortunately, became incapable detecting global (GMPP) PSCs. The system predicted by Spline-GMPPT method using limited sample set current voltage, it maintains position long external stay unchanged. last part, simulation results prove robustness approach.

Language: Английский

A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights DOI Creative Commons
Blessing Olatunde Abisoye, Yanxia Sun, Zenghui Wang

et al.

Renewable energy focus, Journal Year: 2023, Volume and Issue: 48, P. 100529 - 100529

Published: Dec. 20, 2023

The efforts to revolutionize electric power generation and produce clean sustainable electricity have led the exploration of renewable energy systems (RES). This form is replenished cost-effective in terms production maintenance. However, RES, such as solar wind energies, intermittent; this one drawbacks its usage. In order overcome limitation, studies been undertaken forecast availability output. current trending method forecasting generated by RES artificial intelligence (AI) method. with all potential, traditional AI, Artificial Neural Network (ANN), Support Vector Machine (SVM) many more, does not it all. Because this, metaheuristic algorithms are being explored optimization techniques increase performance accuracy these AI methods some challenges models. study presents an insightful survey (traditional metaheuristic) systems. A existing surveyed literature was presented. taxonomy formulated, theoretical backgrounds were Also, various forms improved versions applied optimize classical systems' output surveyed. conceptual framework hybrid application formulated. Finally, discussion, insight, models future directions

Language: Английский

Citations

28
Forecasting Solar Energy: Leveraging Artificial Intelligence and Machine Learning for Sustainable Energy Solutions DOI Open Access

Taraneh Saadati,

Burak Barutçu

Journal of Economic Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

ABSTRACT Integrating solar energy into power grids is essential for advancing a low‐carbon economy, but accurate forecasting remains challenging due to output variability. This study comprehensively reviews models, focusing on how Artificial Intelligence (AI) and Machine Learning (ML) enhance forecast accuracy. It examines the current landscape of forecasting, identifies limitations in existing underscores need more adaptable approaches. The primary goals are analyze evolution AI/ML‐based assess their strengths weaknesses, propose structured methodology selecting implementing AI/ML models tailored forecasting. Through comparative analysis, evaluates individual hybrid across different scenarios, identifying under‐explored research areas. findings indicate significant improvements prediction accuracy through advancements, aiding grid management supporting transition. Ensemble methods, deep learning techniques, show great promise enhancing reliability. Combining diverse approaches with advanced techniques results reliable forecasts. suggests that improving model these integrated methods offers substantial opportunities further research, contributing global sustainability efforts, particularly UN SDGs 7 13, promoting economic growth minimal environmental impact.

Language: Английский

Citations

1
Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization DOI Creative Commons
Chong Liu, P. Balasubramanian, Jingxian An

et al.

npj Clean Water, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 22, 2025

Language: Английский

Citations

1
Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China DOI
Ruyang Yu, Kai Zhang, R. Brindha

et al.

Energy, Journal Year: 2024, Volume and Issue: 296, P. 131146 - 131146

Published: April 3, 2024

Language: Английский

Citations

5
InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation DOI
Mingwei Zhong,

J.M. Fan,

Jianqiang Luo

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 371, P. 123745 - 123745

Published: June 20, 2024

Language: Английский

Citations

5
Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software DOI Creative Commons

Lisbeth Tucumbi,

Jefferson Guano,

Roberto Salazar Achig

et al.

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 601, P. 00051 - 00051

Published: Jan. 1, 2025

The present research focuses on solar radiation prediction, which is important for energy production in thermal and systems. For this purpose, open-source software (Python) a methodology involving the creation, implementation, testing of specific machine learning models random forest (RF) decision tree (DT) were used. metrics used to identify effectiveness predicting coefficient (R 2 ), mean square error (MSE), absolute (MAE). evaluation two methods presented three cases: one, two, seven days. results show that RF model has better than DT, with MAE MSE values 36.96 4238.77, respectively, determination 0.96. study emphasizes importance selecting appropriate based prediction horizon estimate availability improve system planning.

Language: Английский

Citations

0
Prediction of performance and emission features of diesel engine using alumina nanoparticles with neem oil biodiesel based on advanced ML algorithms DOI Creative Commons

M S Aswathanrayan,

N. Santhosh,

H. V. Srikanth

et al.

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

Published: April 12, 2025

Language: Английский

Citations

0
New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models DOI

Amir Hossein Sheikhshoaei,

Ali Sanati

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Abstract Ionic liquids (ILs) as eco-friendly solvents have attracted particular attention in various fields of science including the petroleum industry. Among different families ILs, imidazolium-based ILs been subject many research studies. However, not enough experimental studies were conducted to determine viscosity this family ILs. Therefore, accurate prediction is crucial for their practical applications. This study aims predict and mixtures using critical properties these input parameters. To achieve this, machine learning (ML) models implemented. Furthermore, performance ML predicting IL was compared with a Molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), an Ion-based (ePC-FVT-MB). Graphical statistical analyses revealed that RF model offers lowest error pure while CatBoost performs best mixtures. In addition, sensitivity analysis showed decreases temperature increases pressure. The proposed exhibit high accuracy under varying conditions. Outlier detection Leverage method indicated 95.11% data 94.92% mixed are statistically valid.

Language: Английский

Citations

0
Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model DOI Open Access
Hui Wang, Yan Su,

Danyang Ju

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(21), P. 15594 - 15594

Published: Nov. 3, 2023

Photovoltaic (PV) power generation has brought about enormous economic and environmental benefits, promoting sustainable development. However, due to the intermittency volatility of PV power, high penetration rate may pose challenges planning operation systems. Accurate forecasting is crucial for safe stable grid. This paper proposes a short-term method using K-means clustering, ensemble learning (EL), feature rise-dimensional (FRD) approach, quantile regression (QR) improve accuracy deterministic probabilistic power. The clustering algorithm was used construct weather categories. EL two-layer (TLEL) model based on eXtreme gradient boosting (XGBoost), random forest (RF), CatBoost, long memory (LSTM) models. FRD approach optimize TLEL model, FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM (R-RFL), FRD-CatBoost-LSTM (R-CatBL) models, combine them with results reciprocal error method, in order obtain FRD-TLEL model. QR probability different confidence intervals. experiments were conducted data at time level 15 min from Desert Knowledge Australia Solar Center (DKASC) forecast certain day. Compared other proposed lowest root mean square (RMSE) absolute percentage (MAPE) seasons types. In interval forecasting, 95%, 75%, 50% intervals all have good indicate that exhibits superior performance compared methods.

Language: Английский

Citations

10
Predicting the risk of primary Sjögren's syndrome with key N7-methylguanosine-related genes: A novel XGBoost model DOI Creative Commons
Hui Xie,

Y. Deng,

J. L. Li

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(10), P. e31307 - e31307

Published: May 1, 2024

N7-methylguanosine (m7G) plays a crucial role in mRNA metabolism and other biological processes. However, its regulators' function Primary Sjögren's Syndrome (PSS) remains enigmatic.

Language: Английский

Citations

3