Enhanced Artificial Rabbits Algorithm Integrating Equilibrium Pool to Support PV Power Estimation via Module Parameter Identification

Smaili, Idris H.; Moustafa, Ghareeb; Almalawi, Dhaifallah R.; Ginidi, Ahmed; Shaheen, Abdullah M.; Mansour, Hany S. E.

doi:10.1155/2024/8913560

International Journal of Energy Research

2024

DOI: 10.1155/2024/8913560

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Enhanced Artificial Rabbits Algorithm Integrating Equilibrium Pool to Support PV Power Estimation via Module Parameter Identification

Idris H. Smaili,

Ghareeb Moustafa,

Dhaifallah R. Almalawi

et al.

Abstract: This paper proposes a novel innovative version of enhanced artificial rabbit optimization (EARO) algorithm integrating an equilibrium pool (EP) that consists of the best solutions. Furthermore, the detour foraging and hiding mechanisms are modified to amplify the search capability. These modifications enable the algorithm to dynamically focus on exploring various randomized directions emanating from the EP. The proposed EARO is designed to investigate the PV module characteristics identification issue. To obta… Show more

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Cited by 3 publications

References 99 publications

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Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting

Atiea,

Shaheen,

Alassaf

et al. 2024

International Journal of Energy Research

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Sustainable energy management hinges on precise forecasting of renewable energy sources, with a specific focus on 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 Solar dataset encompassing PV output power and meteorological variables from sensors. The aim is to develop a distinctive hybrid predictive model framework by integrating feature selection techniques with various regression algorithms. This model, referred to as the PV power generation predictive model (PVPGPM), utilizes meteorological data specific to the DKASC. In this study, various feature selection techniques are implemented, including Pearson correlation (PC), variance inflation factor (VIF), mutual information (MI), step forward selection (SFS), backward elimination (BE), recursive feature elimination (RFE), and embedded method (EM), to identify the most influential factors for PV power prediction. Furthermore, a hybrid predictive model integrating multiple regression algorithms is introduced, including linear regression, ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Elastic Net, Extra Trees Regressor, random forest regressor, gradient boosting (GB) regressor, eXtreme Gradient Boosting (XGBoost) Regressor, and a hybrid model thereof. Extensive experimentation and evaluation showcase the effectiveness of the proposed approach in achieving high prediction accuracy. Results demonstrate that the hybrid model comprising XGBoost Regressor, Extra Trees Regressor, and GB regressor surpasses other regression algorithms, yielding a minimal root mean square error (RMSE) of 0.108735 and the highest R‐squared (R2) value of 0.996228. The findings underscore the importance of integrating meteorological insights into renewable energy forecasting for sustainable energy planning and management.

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Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting

Atiea,

Shaheen,

Alassaf

et al. 2024

International Journal of Energy Research

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Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

Alsaggaf,

Gafar,

Sarhan

et al. 2024

Applied Sciences

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The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

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Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

Chaib,

Tadj,

Choucha

et al. 2024

Processes

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The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10−4, 7.478488 × 10−4), (1.430320 × 10−2, 1.427010 × 10−2), and (2.220075 × 10−3, 2.061273 × 10−3), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence.

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Enhanced Artificial Rabbits Algorithm Integrating Equilibrium Pool to Support PV Power Estimation via Module Parameter Identification

Cited by 3 publications

References 99 publications

Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting

Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

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