With increasing installations of photovoltaic (PV) systems, interest in power forecasting has also increased. Inaccurate forecasts would result in substantial economic losses and system reliability issues. The correlation between weather variables and PV power is critical to ensure the efficient use of energy in PV systems. A key step toward accurate power forecasting is estimating the output from a PV system based on known environmental input data. In this research, all available weather data are used to predict the PV power. Meteorological and power data are then analyzed using a statistical approach to identify the order of significance of the input variables. Then, a predictive model is suggested as a function of irradiance, ambient temperature, wind speed, and relative humidity. The model produces a root mean square error of 4.957% and a mean absolute percentage error of 5.468% during the measurement period and over the entire range of irradiation.

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R²-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R²-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.

In this study, long-term reliability tests for high-power-density photovoltaic (PV) modules were introduced and analyzed in accordance with IEC 61215 and light-combined damp heat cycles, such as DIN 75220. The results indicated that post light soaking procedure, light-combined damp heat cycles caused a 3.51% power drop, while IEC standard tests (DH1000 and TC200) caused only 0.87% and 1.32% power drops, respectively. IEC 61215 failed to assess the long-term reliability of the high-power-density PV module, such as the passivated emitter rear cell. Additionally, based on the combined test, the latent heat ($Q_{mod}$) of the module was introduced to predict its degradation rate and to fit the prediction curve of the product guaranteed by the PV module manufacturers. $Q_{mod}$ facilitates in predicting a PV module’s lifespan according to the environmental factors of the actual installation area. The $Q_{mod}$ values of the PV stations in water environments, such as floating and/or marine PVs, indicated that they would last 7.2 years more than those on a rooftop, assuming that latent heat is the only cause of deterioration. Therefore, extending module life and improving power generation efficiency by determining installation sites to minimize latent heat would be advantageous.