Energy Systems, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 30, 2024
Язык: Английский
Results in Engineering, Год журнала: 2025, Номер unknown, С. 103822 - 103822
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2
Electronics, Год журнала: 2024, Номер 13(9), С. 1611 - 1611
Опубликована: Апрель 23, 2024
A recent optimization algorithm, the Rime Optimization Algorithm (RIME), was developed to efficiently utilize physical phenomenon of rime-ice growth. It simulates hard-rime and soft-rime processes, constructing mechanisms puncture search. In this study, an enhanced version, termed Modified RIME (MRIME), is introduced, integrating a Polynomial Differential Learning Operator (PDLO). The incorporation PDLO introduces non-linearities enhancing its adaptability, convergence speed, global search capability compared conventional approach. proposed MRIME algorithm designed identify photovoltaic (PV) module characteristics by considering diverse equivalent circuits, including One-Diode Model (ONE-DM) Two-Diode TWO-DM, determine unspecified parameters PV. approach method using two commercial PV modules, namely STM6-40/36 R.T.C. France cell. simulation results are juxtaposed with those from contemporary algorithms based on published research. outcomes related also in relation various existing studies. indicate that demonstrates substantial improvement rates for cell, achieving 1.16% 18.45% ONE-DM, respectively. For it shows significant reaching 1.14% 50.42%, comparison previously results, establishes superiority robustness.
Язык: Английский
Процитировано
10
Heliyon, Год журнала: 2024, Номер 10(16), С. e35771 - e35771
Опубликована: Авг. 1, 2024
The primary objective of this study is to investigate the effects Fractional Order Kepler Optimization Algorithm (FO-KOA) on photovoltaic (PV) module feature identification in solar systems. Leveraging strengths original KOA, FO-KOA introduces fractional order elements and a Local Escaping Approach (LEA) enhance search efficiency prevent premature convergence. FO element provides effective information past expertise sharing amongst participants avoid converging. Additionally, LEA incorporated boost procedure by evading local optimization. single-diode-model (SDM) Double-diode-model (DDM) are two different equivalent circuits that used for obtaining unidentified parameters PV. Applied KC-200, Ultra-Power-85, SP-70 PV modules, compared KOA technique contemporary algorithms. Simulation results demonstrate FO-KOA's remarkable average improvement rates, showcasing its significant advantages robustness over earlier reported methods. proposed demonstrates exceptional performance, outperforming existing algorithms 94.42 %–99.73 % optimizing cell parameter extraction, particularly KC200GT module, consistent superiority robustness. Also, validated SDM DDM well-known RTC France cell.
Язык: Английский
Процитировано
10
Energy Conversion and Management, Год журнала: 2024, Номер 314, С. 118705 - 118705
Опубликована: Июнь 24, 2024
Язык: Английский
Процитировано
8
Electronics, Год журнала: 2023, Номер 12(24), С. 4990 - 4990
Опубликована: Дек. 13, 2023
This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction solar photovoltaic (SPV) systems. The foraging manner dwarf mongoose animals (DMAs) motivated DMO’s primary design. It makes use distinct DMA societal groups, including alpha category, scouts, babysitters. female initiates chooses path, bedding places, distance travelled group. newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, its modifying approach been led some extent by amended alpha. For two diverse SPV modules, Kyocera KC200GT R.T.C. France proposed is used as opposed DMO efficiently estimate characteristics. By employing technique, simulation results improve electrical characteristics minimization root mean square error value (RMSE) compare efficiency algorithm other reported methods. Based on that, outperforms standard DMO. In terms average efficiency, module 91.7%, 84.63%, 75.7% single-, double-, triple-diode versions, respectively. employed technique R.T.C system success rates 100%, 96.67%, 66.67%, while are 6.67%, 10%, 0% models,
Язык: Английский
Процитировано
19
International Journal of Energy Research, Год журнала: 2024, Номер 2024(1)
Опубликована: Янв. 1, 2024
This paper proposes a novel innovative version of enhanced artificial rabbit optimization (EARO) algorithm integrating an equilibrium pool (EP) that consists the best solutions. Furthermore, detour foraging and hiding mechanisms are modified to amplify search capability. These modifications enable dynamically focus on exploring various randomized directions emanating from EP. The proposed EARO is designed investigate PV module characteristics identification issue. To obtain nine parameters triple diode model (TDM) while taking into account three distinct real‐world modules, utilized evaluated in comparison with standard ARO. tested different modules: Ultra 85‐P panel, PVM_752GaAs, RTC France. results corresponding compared respect several published latest studies. simulation show shows significant overall improvement rates for each modules. A validation common SDM DDM France assessed which illustrates superiority robustness over recent results.
Язык: Английский
Процитировано
7
International Journal of Energy Research, Год журнала: 2024, Номер 2024(1)
Опубликована: Янв. 1, 2024
Sustainable energy management hinges on precise forecasting of renewable sources, with a specific focus solar power. To enhance resource allocation and grid integration, this study introduces an innovative hybrid approach that integrates meteorological data into prediction models for photovoltaic (PV) power generation. A thorough analysis is performed utilizing the Desert Knowledge Australia Solar Centre (DKASC) Hanwha dataset encompassing PV output variables from sensors. The aim to develop distinctive predictive model framework by integrating feature selection techniques various regression algorithms. This model, referred as generation (PVPGPM), utilizes DKASC. In study, are implemented, including Pearson correlation (PC), variance inflation factor (VIF), mutual information (MI), step forward (SFS), backward elimination (BE), recursive (RFE), embedded method (EM), identify most influential factors prediction. Furthermore, multiple algorithms introduced, linear regression, ridge Least Absolute Shrinkage Selection Operator (LASSO) Elastic Net, Extra Trees Regressor, random forest regressor, gradient boosting (GB) eXtreme Gradient Boosting (XGBoost) thereof. Extensive experimentation evaluation showcase effectiveness proposed in achieving high accuracy. Results demonstrate comprising XGBoost GB regressor surpasses other algorithms, yielding minimal root mean square error (RMSE) 0.108735 highest R ‐squared ( 2 ) value 0.996228. findings underscore importance insights sustainable planning management.
Язык: Английский
Процитировано
6
Alexandria Engineering Journal, Год журнала: 2024, Номер 104, С. 95 - 114
Опубликована: Июнь 21, 2024
Язык: Английский
Процитировано
5
Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110150 - 110150
Опубликована: Фев. 11, 2025
Процитировано
0
Journal of Computational Electronics, Год журнала: 2025, Номер 24(2)
Опубликована: Фев. 16, 2025
Язык: Английский
Процитировано
0