Temperature dependent emission spectra and decay times of trivalent dysprosium (${\mathrm{Dy}}^{3+}$) activated ${\mathrm{Y}}_4{\mathrm{Al}}_2{\mathrm{O}}_9$ (YAM) crystals have been studied for the first time (to our knowledge). The ratio of emission lines intensity can be used in temperature measurements, as it is not dependent on the variability of absolute intensity. The Boltzmann model was applied for modeling the temperature variation of the ${{}^4\mathrm{I}}_{15/2}$ and ${{}^4\mathrm{F}}_{9/2}$ states emissions relative intensities 455 and 481 nm, respectively. The calculated approximation gives highest sensor sensitivity of about $3\times {10}^{-3}°{\mathrm{C}}^{-1}$ for the 600°C–800°C range, which allows for an expectation of usefulness of ${\mathrm{Dy}}^{3+}\text{:}\mathrm{YAM}$ in high-temperature luminescence thermometry. Also, the measured decay times are suitable for temperature sensing.

Temperature-dependent emission spectra and fluorescence dynamics profiles have been investigated in Pr³⁺:Y₄Al₂O₉ crystals in order to better understand the processes responsible for quenching of the praseodymium ³P₀ emissions. The cross-relaxation transfer rates were experimentally determined as a function of temperature. Using the rate equations formalism, the dynamics of the observed emissions were modeled. Basing on comparison between the measured and calculated decays, the energy transfer rates between Pr³⁺ ions were evaluated. The role of the backward process in explanation of the complicated character of ³P₀ decays and its temperature dependence, especially its unexpectedly slow decaying component, were established.

This paper concerns very-short-term (5-Minute) forecasting of photovoltaic power generation. Developing the methods useful for this type of forecast is the main aim of this study. We prepared a comprehensive study based on fragmentary time series, including 4 full days, of 5 min power generation. This problem is particularly important to microgrids’ operation control, i.e., for the proper operation of small energy micro-systems. The forecasting of power generation by renewable energy sources on a very-short-term horizon, including PV systems, is very important, especially in the island mode of microgrids’ operation. Inaccurate forecasts can lead to the improper operation of microgrids or increasing costs/decreasing profits for microgrid operators. This paper presents a short description of the performance of photovoltaic systems, particularly the main environmental parameters, and a very detailed statistical analysis of data collected from four sample time series of power generation in an existing PV system, which was located on the roof of a building. Different forecasting methods, which can be employed for this type of forecast, and the choice of proper input data in these methods were the subject of special attention in this paper. Ten various prognostic methods (including hybrid and team methods) were tested. A new, proprietary forecasting method—a hybrid method using three independent MLP-type neural networks—was a unique technique devised by the authors of this paper. The forecasts achieved with the use of various methods are presented and discussed in detail. Additionally, a qualitative analysis of the forecasts, achieved using different measures of quality, was performed. Some of the presented prognostic models are, in our opinion, promising tools for practical use, e.g., for operation control in low-voltage microgrids. The most favorable forecasting methods for various sets of input variables were indicated, and practical conclusions regarding the problem under study were formulated. Thanks to the analysis of the utility of different forecasting methods for four analyzed, separate time series, the reliability of conclusions related to the recommended methods was significantly increased.

The paper describes the performance of the grid-connected 1-kW PV system installed on a grammar school in Poland after eight years of operation. It was designed and installed in year 2000 and from year 2001 it is fully monitored according to guidelines of standard IEC 61724. System was build from 20 a-Si modules (Millennia MST 50 MV by BP Solar) and Sunny Boy inverter. Monitoring system consists of set of meteorological and electrical sensors connected to Sunny Boy Plus Controller supported by PC computer. During eight years of operation, system has produced 4430 kWh of energy. Due to the process of stabilization of electrical parameters of amorphous silicon modules, in first three years of system work, PR values are decreasing. After the end of that process PR is in range from 0.52 to 0.6. Analyzing that value one must remember that due to damage of two modules in second year of system work, its actual nominal power is lower (900 W) that the one that was considered during the calculations (1000 W). Everything that has been written about PR also applies to System Efficiency. Its value is about 3.5%. The inverter efficiency is about 92%. Data gathered by the monitoring system also enabled to perform wide range of analysis of the influence of meteorological parameters, such as irradiance on both horizontal and module plane or temperature, on the system parameters, such as System and PV Generator Losses, System and Inverter Efficiencies, Performance Ratio, or system output electrical parameters, such as DC and AC output power and output DC voltage. The analysis includes also measurement by portable l-V curve tester and infrared camera.