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The development of solar energy at a regional scale necessitates a thorough understanding of available resources. Cuba, facing prolonged economic, environmental, and energy crises, urgently needs to enhance its sustainability through solar energy. Although solar resource mapping is widespread, Cuba lacks extensive field measurements, often relying on models that may not be ideally suited for large regions, like Matanzas province. Spanning over 12,000 km² with nearly 150 km between its northern and southern extremes, Matanzas presents challenges for high-resolution solar mapping. This study introduces a methodology that integrates various methods and databases to achieve the maximum resolution in the resulting solar map. This approach is designed for large areas, where conventional high-resolution models fall short. By optimizing calculation times and parameterizing the entire surface latitudinally, a high-resolution solar resource map for Matanzas has been developed. This map significantly enhances the understanding of solar resources in Cuba and enables the proposal of new methodologies for analyzing solar potential in similarly large regions. [ABSTRACT FROM AUTHOR]

Intermittent solar irradiance due to passing clouds poses challenges for integrating solar energy into existing infrastructure. By making use of intrahour nowcasts (very short‐term forecasts), changing conditions of solar irradiance can be anticipated. All‐sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. Herein, a deep learning (DL) model for solar irradiance nowcasts based on the transformer architecture is presented. The model is trained end‐to‐end using sequences of sky images and irradiance measurements as input to generate point‐forecasts up to 20 min ahead. Further, the effect of integrating this model into a hybrid system, consisting of a physics‐based model and smart persistence, is examined. A comparison between the DL and two hybrid models (with and without the DL model) is conducted on a benchmark dataset. Forecast accuracy for deterministic point‐forecasts is analyzed under different conditions using standard error metrics like root‐mean‐square error and forecast skill. Furthermore, spatial and temporal aggregation effects are investigated. In addition, probabilistic nowcasts for each model are computed via a quantile approach. Overall, the DL model outperforms both hybrid models under the majority of conditions and aggregation effects. [ABSTRACT FROM AUTHOR]

Soiling can greatly reduce both the efficiency of photovoltaic (PV) installations and the signals of radiometers. The knowledge of the current soiling losses of a PV installation can be used to optimize the cleaning schedule and to avoid false alarms related to other issues that might cause underperformance. Underperformance can be detected by comparing measured to modeled PV production derived using pyranometer or reference cell measurements. Soiled pyranometers or reference cells lead to too low modeled PV production so that PV soiling or other errors might not be detected. So far, soiling sensors either require frequent cleaning or they use indirect measurements to derive the soiling loss (e.g., analysis of backscattering signal or imaging of dust on a glass surface). Currently, the soiling loss of pyranometers or outdoor reference cells uses the comparison to another frequently cleaned device of the same model. To avoid time‐consuming maintenance of the sensors and to avoid additional sensors as much as possible, we developed a new method for measuring PV and radiometer soiling losses. The method makes use of a characterized lamp that is protected from soiling by a collimator and that illuminates the pyranometer or reference cell each night for some time. Comparing the signals of one night to the signal obtained at a night shortly after the last cleaning of the sensor, its soiling loss can be derived. To validate the measurements of soiling losses for the pyranometer and the reference cell, the soiling losses of the devices are also derived by comparing their signals to those of a clean sensor of the same type. These reference instruments are calibrated relative to the test devices so that deviations indicate the soiling loss of the test sensors. The first outdoor tests with 4 months of data show a good agreement with the reference measurements of the soiling losses. The accuracy of the method is estimated to be similar to that of the reference method, which involves the daily cleaning of the reference devices. [ABSTRACT FROM AUTHOR]

The share of distributed solar power generation is continuously growing. This increase, combined with the intermittent nature of the solar resource creates new challenges for all relevant stakeholders, from generation to transmission and demand. Insufficient consideration of intra‐minute and intra‐hour variabilities might lead to grid instabilities. Therefore, the relevance of nowcasts (shortest‐term forecasts) is steadily increasing. Nowcasts are suitable for fine‐grained control applications to operate solar power plants in a grid‐friendly way and to secure stable operations of electrical grids. In space and time, highly resolved nowcasts can be obtained by all sky imager (ASI) systems. ASI systems create hemispherical sky images. The associated software analyzes the sky conditions and derives solar irradiance nowcasts. Accuracy is the decisive factor for the effective use of nowcasts. Therefore, the goal of this work is to increase the nowcast accuracy by combining ASI nowcasts and persistence nowcasts, which persist with the prevailing irradiance conditions, while maintaining the spatial coverage and resolution obtained by the ASI system. This hybrid approach combines the strengths while reducing the respective weaknesses of both approaches. Results of a validation show reductions of the root mean square deviation of up to 12% due to the hybrid approach. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.solener.2005.03.004 Byline: Jesus Polo, Luis F. Zarzalejo, Lourdes Ramirez, Bella Espinar Keywords: Solar irradiance; Meteosat satellite; Active learning; Ground database quality Abstract: A new technique of filtering solar radiation ground data is proposed for generating models for solar irradiance estimation from geostationary satellite data. The filtering processes consists of an iterative way of selecting the training data set to achieve the best model response. Although in this paper the proposed methodology has been used for solar irradiance modeling, it could be applied to any kind of empirical modeling. The iterative filtering method has proven to have fast convergence and to improve successfully the statistical model response, when applied to hourly global irradiance calculation from satellite-derived irradiances for 13 Spanish locations. Individual statistical models for hourly global irradiance were fitted using the Heliosat I method applied to Meteosat images of 13 Spanish stations for the period 1994-1996. Author Affiliation: Renewable Energy Department, CIEMAT, Avad. Complutense, 22, 28040 Madrid, Spain Article History: Received 26 January 2004; Revised 17 February 2005; Accepted 9 March 2005 Article Note: (miscellaneous) Communicated by: Associate Editor Pierre Ineichen