Optimization of Module Parameters for PV Power Estimation Using a Hybrid Algorithm

Huang, Yann-Chang; Huang, Chao-Ming; Chen, Shin-Ju; Yang, Sung‐Pei

doi:10.1109/tste.2019.2952444

Cited by 37 publications

(22 citation statements)

References 32 publications

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“…Performance evaluators, such as MRE, MAE, MAPE, RMSE, and R 2 [47][48][49], were used to verify their forecasting method. In this work, it was necessary to evaluate the volatility of prediction error and the forecasting accuracy before we placed the results of several single learners into the ensemble model.…”

Section: Performance Evaluationmentioning

confidence: 99%

Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

et al. 2021

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Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

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Section: Performance Evaluationmentioning

confidence: 99%

Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

et al. 2021

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“…But, when tested under strident climatic conditions, resolved most of the parameters like R S , R Sh , I O , and the range of panels. Hence the proposed method is powerful to all strident contributed conditions [22]. Cervellini et al [23]- [24], introduced a novel Genetic Algorithm (GA) to transform the conventional data into designed data, applies to various Kelvins and irradiation (G) zones [25]- [26].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

Arandhakar

Chaudhary²,

Depuru³

et al. 2022

IEEE Access

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Optimization complications are solved using meta-heuristic methods, which transforms the complex data into simplest way and computational process is quite attractive because of their intensification, diversification, and accurate evaluating/computational behavior on the nonlinear data. Among different existing meta-heuristic algorithms, Tabu Search Optimization (TSO) algorithms have robust performance due to escaping strategy from local optimum and extensive extraction ability. Hence, this paper delineates the TSO searching technique for extracting the parasitic parameters of solar Photovoltaic (PV) modules under different climatic stipulations. Double diode design is developed and implemented in the operational aspect based on the extraction issue. The six parameters of the solar cell, i.e., I Ph , I 01 , I 02 , R S , R P , a 1 are emulated and the obtained data is investigated using TSO approach, at the same time the extracted data is compared with the effective selected metaheuristic algorithms such as Gravitational Search Algorithm (GSA), Lightning Search Algorithm (LSA), Pattern Search (PS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Compared to the existing algorithms, TSO has very less computational time to extract all the six parameters.

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“…Physical methods establish a physical model for PV power generation between the input and output to convert environmental variables, such as irradiance and temperature, into a power output. Physical model involves either an ideal model [11], a simple power model [12], an experimental model [13], a single-diode model [14][15][16][17][18][19][20][21][22][23][24], a two-diode model [22][23][24][25][26][27], or a three-diode model [28,29]. The single-diode model with five parameters has a simpler architecture, and has been widely used to simulate the electrical behavior of solar cells.…”

Section: Introductionmentioning

confidence: 99%

“…When the parameters that affect the output are selected, an optimization tool is used to accurately estimate the parameters. Several optimization algorithms have been used to estimate the parameters of solar cells, such as the hybrid charged system search algorithm [15], the particle swarm optimization (PSO) method [18], the differential evolution algorithm (DE) [19], the chaos-embedded gravitational search algorithm [21], the bonobo optimizer [22], the improved cuckoo search algorithm [23], the chaotic improved artificial bee colony algorithm [24], the ranking-based whale optimizer (WO) [28], and the grasshopper optimization algorithm [29]. As mentioned above, most studies use different optimization tools to solve the parameter estimation problem.…”

Section: Introductionmentioning

confidence: 99%

A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

2022

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This study presents a parameter estimation method that uses an enhanced gray wolf optimizer (EGWO) to optimize the parameters for a two-diode photovoltaic (PV) power generation system. The proposed method consists of three stages. The first stage converts seven parameters for the two-diode model into 17 parameters for different environmental conditions, which provides more precise parameter estimation for the PV model. A PV power generation model is then established to represent the nonlinear relationship between inputs and outputs. The second stage involves a parameter sensitivity analysis and uses the overall effect method to remove the parameters that have smaller effect on the output. The final stage uses an enhanced GWO that is associated with measurement data to optimally estimate the parameters that are selected in the second stage. When the parameters are estimated, the predicted value for the PV power output is calculated for specific values of solar irradiation and module temperature. The proposed method is verified on a 200 kWp PV power generation system. To confirm the feasibility of the proposed method, the parameter estimation before and after optimization are compared, and these results are compared with other optimization algorithms, as well as those for a single-diode PV model.

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Optimization of Module Parameters for PV Power Estimation Using a Hybrid Algorithm

Cited by 37 publications

References 32 publications

Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

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