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Photovoltaic (PV) power forecasting has become a key tool for the intelligent management of electrical grids. Since the largest source of error in PV power forecasting originates from uncertainties in solar irradiance prediction, improving the accuracy of solar irradiance forecasts has emerged as an active research topic. This study evaluates multiple random tree-based model versions using a challenging dataset collected at globally distributed stations, spanning elevations from sea level to nearly 4000 m and covering a wide range of climate classes. The originality of the study lies in the synergistic contribution of two elements: the innovative inclusion of diffuse irradiance among the predictors and a comparative analysis of forecast quality across lowland and mountainous locations. In such environments, accurate solar resource forecasting is particularly important for the intelligent management of stand-alone PV systems deployed at high altitudes and in remote, off-grid areas. Overall, the results identify Extremely Randomized Trees (XTRc) as the best-performing model. XTRc achieves Skill Scores ranging from 0.087 to 0.298 across individual stations. The model accuracy remains high even at mountain stations, provided that sky-condition variability is low.

In the operation stage of a photovoltaic (PV) power plant, the output power often differs from the expected value. This deviation is sometimes caused by the so-called spectral mismatch, i.e. the difference between the actual solar radiation spectrum and the standard AM1.5G spectrum, under which the efficiency of a PV module is measured. Spectral mismatch appears as a hidden source of uncertainty in PV production, since for the same measured value of solar irradiance, the output power fluctuates depending on the atmospheric parameters. In this study, the magnitude of the uncertainty induced by the spectral mismatch is assessed. The study was conducted following an innovative methodology based on rigorous simulations of solar spectrum in real geometric-frames and various atmospheric conditions (with a focus on aerosols). The analysis is performed in terms of the familiar spectral factor. Furthermore, the influence of spectral mismatch on PV efficiency is evaluated. Overall findings indicate that under certain conditions spectral mismatch causes a gain or loss in PV efficiency within a rough margin of 10%. Marginal loss can be recorded even under mild conditions: small tilt angle, the Sun close to the horizon and an atmosphere moderately loaded with fine absorbing aerosol.

The separation models are tools used in solar engineering to estimate direct normal (DNI) and diffuse horizontal (DHI) solar irradiances from measurements of global solar irradiance (GHI). This paper proposes two empirical separation models that stand out owing to their simple mathematical formulation: a rational polynomial equation. Validation of the new models was carried out against data from 36 locations, covering the four major climatic zones. Five current top minute-scale separation models were considered references. The tests were performed on the final products of the estimation: DNI and DHI. The first model (M1) operates with eight predictors (evaluated from GHI post-processed measurements and clear-sky counterpart estimates) and constantly outperforms the already established models. The second model (M2) operates with three predictors based only on GHI measurements, which gives it a high degree of accessibility. Based on a statistical linear ranking method according to the models’ performance at every station, M1 leads the hierarchy, ranking first in both DNI and DHI estimation. The high accessibility of the M2 does not compromise accuracy; it is proving to be a real competitor in the race with the best-performing current models.

The one-diode model is probably the most common equivalent electrical circuit of a real crystalline solar cell. Extensive research has focused on extracting model parameters from measurements performed in standard test conditions (STC), aiming to replicate the current-voltage characteristics (I-V). This study started from finding that, for the same solar cell, different scientific reports yield significantly different sets of parameters, all allowing for highly accurate replication of the measured I-V characteristics. This observation raises a big question: What is the true physical set of parameters? The present study attempts to address this question. For this purpose, a numerical experiment was conducted. The results show that there is an infinity of distinct sets of parameters that can replicate the I-V characteristics at STC via the one-diode model equation. The diode saturation current IS and the diode ideality factor compensate each other to preserve the open-circuit voltage VOC, always an input data point. Some possible approaches (e.g., the link between VOC and IS) that can lead to the physical set of parameters are discussed, highlighting their strengths and weaknesses. There is enough room for future research on finding a universal approach able to guarantee the accurate extraction of the one-diode model physical parameters.

Most studies have focused on extending the applicability of the Ångström–Prescott equation and improving its accuracy in estimating solar irradiation. Only a limited number of studies have addressed atmospheric climatology using the Ångström–Prescott equation. In contrast, this study reformulates the Ångström–Prescott equation to explore its potential for extracting long-term atmospheric transparency information from radiometric measurements. It introduces a new annual formulation of the Ångström–Prescott equation derived from its common monthly version. While the formal structure is preserved, the equation shifts from its usual role, as a solar irradiation estimator, to a new role, as a predictor of long-term atmospheric transparency. The revised equation naturally defines an annual effective sunshine duration, which assigns greater weight to relative sunshine during summer months than during winter months. To enable prediction, the revised Ångström–Prescott equation is combined with Gaussian process regression. The equation provides the historical annual time series, while Gaussian process regression predicts future values and quantifies their associated uncertainty. To demonstrate the predictive capability of the method, it is applied to the analysis and prediction of four annual parameters characterizing atmospheric transparency: mean clear-sky atmospheric transparency, mean cloud transmittance, mean atmospheric transparency, and effective relative sunshine duration. The analysis is conducted using radiometric data collected at 14 stations distributed across Europe. Predictions for the upcoming decade (2024–2033) indicate that, at most stations, mean atmospheric transparency is expected to remain stable or change within approximate margins of −5% to +10%.

The knowledge of the solar irradiation components is required by most solar applications. When only the global horizontal irradiance is measured, this one is typically broken down into its fundamental components, beam and diffuse, by applying an empirical separation model. This study proposes a semi-analytical model for diffuse fraction, defined as the ratio of diffuse to global solar irradiance. Starting from basic knowledge, a general equation for diffuse fraction is derived. Clearness index, relative sunshine, and the clear-sky atmospheric transmittance are highlighted as robust predictors. Thus, the model equation implicitly provides hints for developing accurate empirical separation models. The proposed equation is quasi-universal, allowing for temporal (from 1-min to 1-day) and spatial (site specificity) customization. As a proof of theory, the separation quality is discussed in detail on the basis of radiometric data retrieved from Baseline Surface Radiation Network (BSRN), station Magurele, Romania. For temperate continental climate, overall results show for the diffuse fraction estimation a maximum possible accuracy around 7%, measured in terms of normalized root mean square error. One of the many options of implementing the semi-analytical model is illustrated in a case study.

Due to its simplicity, the one-diode model is commonly used for modeling the operation of photovoltaic (PV) modules at standard test conditions (STC). However, its inherent implicit nature often presents challenges in modeling PV energy production. In this paper, the innovative explicit one-diode model developed by us over time is adapted for estimating PV power production under real weather conditions. Simple yet accurate equations for calculating the energy output of a PV generator equipped with a maximum power point tracking (MPPT) system are proposed. The model’s performance is assessed under various normal and harsh operating conditions against measured data collected from the experimental setup located at the Solar Platform at West University of Timisoara, Romania. As an application of the new equation for maximum power, this paper presents a case study where the energy loss in the absence of an MPPT system is evaluated based on atmospheric and sky conditions.

The broadband spectrum of solar radiation is commonly characterized by indices such as the average photon energy (APE) and the blue fraction (BF). This work explores the effectiveness of the two indices in a narrower spectral band, namely the ultraviolet (UV). The analysis is carried out from two perspectives: sensitivity to the changes in the UV spectrum and the uniqueness (each index value uniquely characterizes a single UV spectrum). The evaluation is performed in relation to the changes in spectrum induced by the main atmospheric attenuators in the UV band: ozone and aerosols. Synthetic UV spectra are generated in different atmospheric conditions using the SMARTS2 spectral solar irradiance model. The closing result is a new index for the signature of the solar UV radiation spectrum. The index is conceptually just like the BF, but it captures the specificity of the UV spectrum, being defined as the fraction of the energy of solar UV radiation held by the UV-B band. Therefore, this study gives a new meaning and a new utility to the common UV-B/UV ratio.

Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky imagery, is proposed. This study is conducted with high-quality data and high-resolution sky images recorded on the Solar Platform of the West University of Timisoara, Romania. Atmospheric factors that contribute to improving or reducing the quality of forecasts are discussed. Generally, the statistical models gain a small skill score across all forecast horizons (5 to 30 min). The machine-learning-based methods perform best at smaller forecast horizons (less than 15 min), while the all-sky-imagery-based model performs best at larger forecast horizons. Overall, for forecast horizons between 10 and 30 min, the weighted forecast ensemble with frozen coefficients achieves a skill score between 15 and 20%.

This research provides an innovative empirical model for solar irradiance estimation under clear sky conditions. The new model adjusts the estimates of G-EM clear-sky solar irradiance model (Goilean et al. AWUT-Physics 66, 2024, 241) with respect to site altitude. The study was conducted based on a synthetically obtained dataset, generated using two high-performance clear-sky solar irradiance parametric models. Preliminary tests were performed against high quality clear-sky radiometric data. The results demonstrate that explicit insertion of altitude as an input parameter of G-EM enhances their performance in mountain locations, thus expanding its geographical area of applicability.