Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.

The present work proposes an enhanced method of investigation and optimization photovoltaic (PV) modules by approaching and using MPPT (Maximum Power Point Tracking) technique to improve their output power. The performance of the PV panels is strongly influenced by the operating conditions, especially regarding the solar irradiance, temperature, configuration, and the shading (due to a passing cloud or neighboring buildings); all these cause, both on energy conversion loss, and further on non-linearity of the I-V characteristics. From this reason, the present study could have a high relevance based on the improvement of the performances (including the efficiency) of the shaded photovoltaic panels and would quantify the impact of a complex approach represented by numerical modeling and experimental validation. For a better understanding of these issues determined by partial shading, and improvement of MPP tracking, it is required to study the behavior of individual panels. For the best accuracy of the implemented models a comparative analysis and optimized method of the PV modules was considered based on: (1) the influence of temperature and solar irradiance and behavior of the PV modules in partial shading conditions; (2) a comparison between the optimized output power of four algorithms (FLC—Fuzzy Logic Controller, P&O—Perturb and Observe, IncCond—Incremental Conductance and RC Ripple Correlation) and the selection of the best one (FLC); (3) discussion of customized/improved fuzzy logic controller (FLC) algorithm on five operation points introduced in order to increase PV module efficiency under fluctuating weather conditions and rapidly changing uncertainties. Furthermore, the FLC provides a set of rules useful for predicting the current-voltage behavior and the maximum power points of shaded photovoltaic modules. This FLC algorithm was implemented in a specialized software, namely MATLAB/Simulink. The authors highlighted the development and implementation of a numerical simulation model for an advanced PV module to determine its behavior under different operating conditions and improve its performance. The essence of the authors’ research and the motivation of this work is described. The authors were able to stabilize and improve the output performance of the PV module. The results concerning the shading effect as well as the shading patterns were developed, demonstrated, and experimentally validated. These results could be applied for the actual photovoltaic installations, respectively complex stand-alone or grid-connected photovoltaic systems.

Silicon-based tandem solar cells incorporating low-cost, abundant, and non-toxic metal oxide materials can increase the conversion efficiency of silicon solar cells beyond their conventional limitations with obvious economic and environmental benefits. In this work, the electrical characteristics of a metal oxide thin-film heterojunction solar cell based on a cuprous oxide (Cu₂O) absorber layer were investigated. Highly Al-doped n-type ZnO (AZO) and undoped p-type Cu₂O thin films were prepared on quartz substrates by magnetron sputter deposition. The electrical and optical properties of these thin films were determined from Hall effect measurements and spectroscopic ellipsometry. After annealing the Cu₂O film at 900 °C, the majority carrier (hole) mobility and the resistivity were measured at 50 cm²/V·s and 200 Ω·cm, respectively. Numerical modeling was carried out to investigate the effect of band alignment and interface defects on the electrical characteristics of the AZO/Cu₂O heterojunction. The analysis suggests that the incorporation of a buffer layer can enhance the performance of the heterojunction solar cell as a result of reduced conduction band offset.

The authors studied a working off-grid type photovoltaic (PV) pumping system for irrigation use. The methodology was based on digitalization analysis and numerical modeling as a preliminary stage. A mathematical model of the PV pumping installation considered the determination of the characteristic equations for all its components. These have been used together with the SISIFO simulation software to achieve the performances of the mechanical and electrical components of an advanced PV pumping system. Its global performance features, namely the monthly energy yield, monthly pumping yield, and monthly total performances (energy and flow rate) were introduced. Digital platform (DP) for PV systems characterized by three advanced technologies—machine learning (ML), digital twin (DT) and artificial intelligence (AI) was developed. The simulation results were discussed for a specific case study conducted for a location in the Southern Romania regarding the irrigation potential, taking into account the main meteorological parameters: solar irradiance and ambient temperature, related to the site. The AI approach was implemented to achieve an optimum operation of the PV pumping system by the use of the maximum power point tracking (MPPT) method and the MATLAB/Simulink software. A unified development of all the components of the PV pumping system using the SISIFO simulation software was established by the authors, with major implications in the development of solar PV installations on large-scale.

This study is aimed at increasing the performance and reliability of silicon-based heterojunction solar cells with advanced methods. This is achieved by a numerical electro-optical modeling and reliability analysis for such solar cells correlated with experimental analysis of the Cu₂O absorber layer. It yields the optimization of a silicon tandem heterojunction solar cell based on a ZnO/Cu₂O subcell and a c-Si bottom subcell using electro-optical numerical modeling. The buffer layer affinity and mobility together with a low conduction band offset for the heterojunction are discussed, as well as spectral properties of the device model. Experimental research of N-doped Cu₂O thin films was dedicated to two main activities: (1) fabrication of specific samples by DC magnetron sputtering and (2) detailed characterization of the analyzed samples. This last investigation was based on advanced techniques: morphological (scanning electron microscopy—SEM and atomic force microscopy—AFM), structural (X-ray diffraction—XRD), and optical (spectroscopic ellipsometry—SE and Fourier-transform infrared spectroscopy—FTIR). This approach qualified the heterojunction solar cell based on cuprous oxide with nitrogen as an attractive candidate for high-performance solar devices. A reliability analysis based on Weibull statistical distribution establishes the degradation degree and failure rate of the studied solar cells under stress and under standard conditions.