Abstract
In the present work, two artificial intelligence-based models were proposed to determine the output power of two types of photovoltaic cells including multicrystalline (multi-) and monocrystalline (mono-). Adaptive neuro-fuzzy inference system (ANFIS) and Least-squares support vector machine (LSSVM) are applied for the output power calculations. The estimation results are very close to the actual data based on graphical and statistical analysis. The coefficients of determination ( R 2 ) of monocrystalline cell output power for LSSVM and ANFIS models are as 0.997 and 0.962, respectively. Additionally, multicells have R 2 values of 0.999 and 0.995 for LSSVM and ANFIS, respectively. The acceptable values for R 2 and various error parameters prove the accuracy of suggested models. The visualization of these comparisons clarifies the accuracy of suggested models. Additionally, the proposed models are compared with previously published machine learning methods. The accurate performance of proposed models in comparison with others showed that our models can be helpful tools for the estimation of output power. Moreover, a sensitivity analysis for the effects of inputs parameters on output power has been employed. The sensitivity output shows that light intensity has more on output power. The outcomes of this study provide interesting tools which have potential to apply in different parts of renewable energy industries.
Highlights
Photovoltaic (PV) cell power generation as a renewable energy source has vital importance, because it is used to overcome the present energy problems and is environment-friendly to overcome the present environmental problems [1,2,3]
There are some valuable studies on renewable energy topics such as experimental investigation on the effect on the mass flow rate of the nanofluid, volume fraction of the nanofluid, and volume of the storage tank on the inletoutlet water temperature difference and the energy efficiency of an evacuated tube solar collector
For graphical comparison, the simultaneous illustrations of predicted and actual output power for monocells and multicells are shown in Figures 3 and 4 for Adaptive neuro-fuzzy inference system (ANFIS) and Least-squares support vector machine (LSSVM) models, respectively
Summary
Photovoltaic (PV) cell power generation as a renewable energy source has vital importance, because it is used to overcome the present energy problems and is environment-friendly to overcome the present environmental problems [1,2,3]. The development and studies in renewable energy technology can reduce the global warming problem and other environmental problems [4]. There are some valuable studies on renewable energy topics such as experimental investigation on the effect on the mass flow rate of the nanofluid, volume fraction of the nanofluid, and volume of the storage tank on the inletoutlet water temperature difference and the energy efficiency of an evacuated tube solar collector. Three machine learning approaches called gene-expression programming, model tree, and multivariate adaptive regression spline were developed for prediction of these target parameters [5]. Sadeghi et al developed a modeling investigation on the evacuated tube solar collector. The geneexpression programming was used to simulate evacuated tube solar collector in various volumes of the thermal storage tanks and solar radiation intensities [6]. Akhter et al evaluated the performance of three PV technologies including thin-film, monocrystalline, and poly-crystalline technologies based on eleven different performance parameters [7]
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