PyranoCam: Simple measurement system for all components of solar irradiance in arbitrary planes

Accurate, robust and cost-efficient measurements of different solar irradiance components in arbitrary planes are of great interest for solar energy applications. A wide range of costeffective and robust measurement systems are currently available on the market. Available measurement techniques exhibit at least one of these shortcomings: intensive maintenance, high acquisition cost, increased deviations or restrictions to single planes (global tilted irradiance). PyranoCam is a robust and inexpensive setup of a thermopile pyranometer and an all-sky imager (ASI) for measurements of GHI, DHI, DNI and GTI (for any arbitrary plane

All-sky imager based irradiance nowcasts: combining a physical and a deep learning mdoel

Improved solar irradiance nowcasts based on all-sky imagers. Hybrid physical and end-to-end machine learning (ML) model. The ML model is based on an multi-modal deep learning model combining an vision transformer (for images) with an time series transformer (for time series data). Skill score improvements >12% points are achieved. Correct detection of cloud ramp rates improved by >8% points

Estimación de la radiación global diaria en zonas de topografía compleja utilizando modelos digitales del terreno e imágenes de Meteosat: comparación de los resultados

Ponencia presentada en: XXIX Jornadas Científicas de la AME y el VII Encuentro Hispano Luso de Meteorología celebrado en Pamplona, del 24 al 26 de abril de 2006.El conocimiento de la radiación solar es muy importante a la hora de diseñar sistemas solares tanto térmicos, como fotovoltaicos. En escalas locales, la topografía es el factor más importante modulador de la radiación solar en superficie. En este trabajo se estima la radiación global diaria en todo tipo de condiciones de cielo, en zonas que presentan una topografía compleja. Para ello se utilizará una metodología basada en Modelos Digitales del Terreno (MDT) a partir, por un lado, de medidas piranométricas y, por otro, de imágenes de satélite. Se pone de manifiesto que la aplicación del MDT sobre medidas piranométricas proporciona mejores resultados que las estimaciones a partir de imágenes de satélite, si bien la precisión obtenida (RMSE & MBE) es del mismo orden de magnitud en ambos casos

Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager

Accurate, robust and cost-efficient measurements of diffuse horizontal irradiance (DHI) and global tilted irradiance (GTI) are of great interest for solar energy applications. However, the available measurement techniques exhibit at least one of these shortcomings: restriction of GTI measurement to a single plane, intensive maintenance, high acquisition cost or increased deviations, especially at new measurement sites. To avoid these shortcomings, we suggest a comparably inexpensive and robust setup of a thermopile pyranometer and an all-sky imager (ASI) for measurement of DHI and GTI. The pyranometer measures global horizontal irradiance (GHI) and our method consecutively estimates diffuse sky radiance, DHI, direct normal irradiance (DNI) and GTI, by merging information from the combined setup. The system is developed and validated at two sites in Spain and Germany. Measurement of GTI is benchmarked for seven planes over GTI derived by transposition based on DHI and DNI from a tracker setup with a pyrheliometer and shaded thermopile pyranometer. Our results indicate that the measurement system can be applied at both sites. The proposed method avoids time-consuming radiometric calibrations of the camera by the combination of both sensors and a self-calibration. The measurement system is promising in particular for measurement of GTI. For 10-min average GTI, our approach yields an rRMSD of 1.6...4.8% for planes with tilts in the range of 20°...61°. Thus, at both sites and for all planes, it outperforms the tracker-based transposition yielding 2.3...6.5%. DHI is measured significantly more accurately than reported in previous works using an ASI alone

Combining deep learning and physical models for solar nowcasting

Sudden changes in solar irradiance on a local scale can significantly influence solar power generation. This intermittent characteristic of the solar resource is mainly caused by passing clouds and represents a challenge when solar energy is integrated into the power system. By making use of intra hour nowcasts (very short-term forecasts), changing conditions on solar irradiance can be anticipated, allowing optimized power plant operation and grid integration. All-sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. However, the benefit of these nowcasting systems heavily depends on the accuracy of the predictions. In a previous work, a hybrid model combining physics-based and persistence nowcasts has proven to be advantageous. In this work, we present a novel deep learning (DL) model based on the transformer architecture for solar irradiance nowcasts and show that integrating this model into the hybrid model further improves the nowcast quality significantly. While the physics-based nowcasts are derived from a pipeline of processing steps to model clouds and anticipating their impact on solar irradiance, the DL model is completely data-driven and trained end-to-end using sequences of sky images and groundbased irradiance measurements as input. For comparison to the literature, evaluation is carried out on a benchmark dataset of 2019 from the same site. First, the nowcast quality of the DL model is analyzed independently on standard forecasting error metrics like root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and forecast skill. For computing the forecast skill, we used the so-called smart persistence (SP) as reference model. Reaching scores of over 28%, the DL model itself already outperforms the previous hybrid model in terms of RMSE. Next, the hybrid model, combining physics-based, DL and SP nowcasts, is evaluated on the same dataset using the same metrics. Compared to the previous hybrid model, the new hybrid model shows significant improvement over all metrics