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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts., Validerad;2023;Nivå 2;2023-05-29 (hanlid)

The parameter determination based on experimental data aids in providing an accurate assessment for predicting the output current of the PV cells. This may be extremely helpful both practically and theoretically in the PV system simulation, optimization, and evaluation. Numerous stochastic methods have been widely utilized in recent decades to tackle the parameter extraction optimization problem. Hybrid methods that take advantage of two or more algorithms have been proposed by many to further enhance their accuracy, stability, and overall performance. However, the majority of the methods in the literature concentrate on the methodology, with few studies considering the formulation of the objective function, resulting in a theoretical gap in this area of study. In this work, a new improved arithmetic optimization method based on the adaption of Newton–Raphson and Levenberg–Marquardt damping parameter (IAOANRaLMp) is presented to globally extract the parameter of the single- and double-diode PV models. The experimental findings demonstrate that the proposed IAOANRaLMp approach performs exceptionally well in terms of minimizing error to zero for both models, as indicated by various statistical criteria and comparison with experimental data.

Solar Photovoltaic (PV) system is one of the most significant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efficient Newton Raphson (HAOA ENR ) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efficiently designed to roughly predict the initial root values of the PV equation. Secondly, the Lévy flight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the fluctuation behaviours of the PV models. The experimental findings revealed that the proposed HAOA ENR outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.

Extraction of the photovoltaic (PV) model parameters is critical for forecasting these systems’ energy output. Numerous research have reviewed and presented approaches for figuring out the PV models parameter optimization problem in the literature. However, few studies have been undertaken to construct the objective function, or no review papers have been published on the applied methodologies for solving the equations of nonlinear, multi-variable, and complicated PV models based on the datasheet information or actual experimental data. Therefore, this study seeks to first explore the acquired approaches to solve the equations of PV models. Then, utilizing actual measured laboratory data collected under a variety of environmental circumstances, a hybrid approach for efficiently determining unknown the parameters of the single, double, and three diodes PV model has been developed. The proposed guaranteed convergence arithmetic optimization algorithm based on efficient modified third order Newton Raphson (GCAOAEmNR) method highlights important contributions to the literature in terms of methodology (explorer-exploiter phases) and objective function design. The experimental findings exhibit that the GCAOAEmNR minimizes the error to zero under diverse statistical criteria and environmental conditions. Moreover, the GCAOAEmNR beats all well-published approaches in the literature in terms of accuracy, stability, and convergence rate while requiring a reasonable amount of processing time.

The estimation of the unknown parameters of the photovoltaic (PV) model is crucial for accurately verifying its real performance precisely under a wide range of climatic conditions. This paper presents an approach to determine the nine parameters of the three diode (TD) PV model based on the integration of the guaranteed convergence arithmetic optimization algorithm and Levenberg-Marquardt with adaptive damping nonlinear parameter method named as GCAOAAdLM. The keystone of the GCAOAAdLM model is accomplished by efficaciously enhancing the exploiter-explorer tendency with inclusion of various powerful hybrid strategies in terms the methodology itself. In addition, the objective function is newly designed leveraging on Levenberg-Marquardt with adaptive damping parameter method to accurately determine the initial roots parameters of the TD PV model. The experimental results demonstrate that the proposed GCAOAAdLM can reduce the root mean square error (RMSE), mean bias error (MBE), deviation of solar radiation’s levels (di), test statistical (TS), and absolute error (AE) to zero and the determination coefficient (R2) to 1 for all environmental conditions, with statistical reasons and comparisons against well-published approaches available in the literature.

Standalone photovoltaic (SAPV) systems have been considered as promising and fast development renewable energy sources due to free-noise, easy availability, and low-cost, especially for remote areas. However, the main disadvantages of these systems are low energy conversion and high capital cost. Therefore, many factors should be considered before installing the SAPV systems such as types of PV panels and configurations, mathematical models of PV module, storage battery, environmental criteria, sizing method based on techno-economic objectives, and selection the final optimum configuration. The goals of this paper are to presents a comprehensive review of the recent development in designing SAPV systems based on multi-objective optimization (MOO) and multi-criteria decision-making (MCDM) methodologies, including the mathematical models used in estimating the output power of the PV module and storage battery are also presented. Finally, the techno-economic criteria for assessing the performance of the SAPV system are considered. This will help the designers and customers to choose the most suitable design before installing the SAPV system.