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%.

Models based on Numerical Weather Prediction (NWP) are widely used for the day-ahead forecast of solar resources. This study is focused on the calibration of the hourly global solar irradiation forecasts provided by the Global Forecast System (GFS), a model from the NWP class. Since the evaluation of GFS raw forecasts sometimes shows a high degree of uncertainty (the relative error exceeding 100%), a procedure for reducing the errors is needed as a prerequisite for engineering applications. In this study, a deep analysis of the error sources in relation to the state of the atmosphere is reported. Of special note is the use of sky imagery in the identification process. Generally, it has been found that the largest errors are determined by the underestimation of cloud coverage. For calibration, a new ensemble forecast is proposed. It combines two machine learning approaches, Support Vector Regression and Multi-Layer Perceptron. In contrast to a typical calibration, the objective function is constructed based on the absolute error instead of the traditional root mean squared error. In terms of normalized root mean squared error, the calibration roughly reduces the uncertainty in hourly global solar irradiation by 16%. The study was conducted with high-quality ground-measured data from the Solar Platform of the West University of Timisoara, Romania. To ensure high accessibility, all the parameters required to run the proposed calibration procedures are provided.

PV modules for Building-Integrated Photovoltaic (BIPV) applications are made of different colors aimed at raising the visual aesthetic of the building. But a colored coating applied to the surface of a basic PV module is inherently associated to a decrease in conversion efficiency. From a different perspective, the efficiency of a PV module is evaluated under the industry standard test conditions (STC). Due to spectral mismatch, the efficiency of a PV module operating in outdoor conditions may substantially differ from the standard value evaluated at STC. In this study, the influence of spectral solar irradiance distribution on the colored PV module efficiency is evaluated in terms of spectral factor (SF). SF quantifies the relative power gain or loss caused by the spectral difference from STC. The theory is illustrated with a case study on the main four urban areas in Romania. The actual solar radiation spectrum is estimated with the simple Leckner spectral solar irradiance model, based on atmospheric parameters retrieved from the Aerosol Robotic Network (AERONET). The results emphasize that the aesthetic of BIPV comes at a high energy cost: depending on the color, a coating applied on the surface of a crystalline silicon PV module may reduce its conversion efficiency even by half.

Econophysics is a relatively young discipline, being an interdisciplinary approach that applies methods and tools from physics in the economics, for studying the financial markets, or other economical phenomena. The objective is to create a mapping of the most used keywords in this topic, as well as the connections between countries and authors, based on citations. This article studies the literature on the topic of econophysics, using the Web of Science database. Bibliometric analysis was made with the free software VOSviewer, after extracting the data from 1364 articles, since the conception of the topic in 1996 until the year 2020. The software offered a useful network representation between the main keywords used in the field, or between countries and citations, and finally between authors, based on citations. The results evidenced a constant interest on the topic, with main keywords: ”econophysics”, ”financial markets”, ”dynamics”, ”model” and ”stock market”, and the relationships between countries and authors were shown, with the United States, China and Italy being the leading ones. We also found that the most cited authors are H.E. Stanley, the inventor of the word econophysics, Wei-Xing Zhou and Didier Sornette. This article shows the main concepts used in econophysics, which can serve as an indicator of its directions of research, as well as the top contributors in the field, since its inception. Future directions include expanding the analysis to other databases, or to concepts relevant to econophysics, to identify trends in research and the growth of the field.

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%.