Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations

t Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest

Scale Matters: Attribution Meets the Wavelet Domain to Explain Model Sensitivity to Image Corruptions

Neural networks have shown remarkable performance in computer vision, but their deployment in real-world scenarios is challenging due to their sensitivity to image corruptions. Existing attribution methods are uninformative for explaining the sensitivity to image corruptions, while the literature on robustness only provides model-based explanations. However, the ability to scrutinize models' behavior under image corruptions is crucial to increase the user's trust. Towards this end, we introduce the Wavelet sCale Attribution Method (WCAM), a generalization of attribution from the pixel domain to the space-scale domain. Attribution in the space-scale domain reveals where and on what scales the model focuses. We show that the WCAM explains models' failures under image corruptions, identifies sufficient information for prediction, and explains how zoom-in increases accuracy.Comment: main: 9 pages, appendix 19 pages, 32 figures, 5 table

Evaluating the spatial and temporal variations of the performance of CAMS Radiation Service and HelioClim-3 databases of surface irradiation in Germany

International audienceSatellite-derived databases of the surface solar irradiance (SSI) have become an essential source of information for various applications in solar energy. Assessing the accuracy of these data by comparison with reference in-situ measurements is therefore ever gaining in importance. Several authors have reported that performances of a given database differ from one site to another depending on the geographical region, topography, orography, climate, viewing angle from the satellite.. . A good understanding of the spatial and temporal variation of the SSI estimation error is key to allow end-user to have an appropriate level of expectation on the accuracy of this data. This knowledge can also be very important for the further developments of the algorithms. The present work contributes to this objective by extending the validation works carried out in the last years for numerous regions (Europe, Brazil, Egypt, Arabic Peninsula, Morocco and The Netherlands) to Germany. We consider two databases: the CAMS Radiation Service version 3 (abbreviated as CAMS-Rad) and the HelioClim-3 version 5 (abbreviated as HC3v5) that are widely used by academics and practitioners. The present communication focuses on several stations located in Germany operated by the Deutscher Wetterdienst (DWD). They are spread over the country, thus allowing the study of the spatial consistency of the performance of each database. Measurements of 10 min means of global irradiance made by pyranometers (CM11 and CM21) and SCAPP set publicly available by the DWD for the period 2010-2018 (9 years) have been used for the validation. Measurements were quality-checked using the method described by Roesch et al. (2011). Satellite-derived SSI estimates were collected from the SoDa web site (www.soda-pro.com) for the same locations and same instants of measurements for both databases. CAMS-Rad uses the Heliosat-4 method with different inputs: the clear-sky radiation is evaluated using Copernicus Atmosphere Monitoring Service (CAMS) information on the aerosol, ozone and water vapour contained in the atmosphere, the cloud attenuation is considered using cloud optical properties retrieved every 15 min from Meteosat imagery using APPOLO. The second database is the HelioClim-3v5 that is derived from Meteosat images using the Heliosat-2 method, McClear and CAMS products. For each database, standard error metrics are computed at each station. A particular attention is paid in the presentation of the validation results to evaluate the effects of different parameters such as e.g. the solar elevation and the clearness index on the error. A focus of this work is laid on the consistency of the errors with space and time

PyPVRoof: a Python package for extracting the characteristics of rooftop PV installations using remote sensing data

Photovoltaic (PV) energy grows at an unprecedented pace, which makes it difficult to maintain up-to-date and accurate PV registries, which are critical for many applications such as PV power generation estimation. This lack of qualitative data is especially true in the case of rooftop PV installations. As a result, extensive efforts are put into the constitution of PV inventories. However, although valuable, these registries cannot be directly used for monitoring the deployment of PV or estimating the PV power generation, as these tasks usually require PV systems {\it characteristics}. To seamlessly extract these characteristics from the global inventories, we introduce {\tt PyPVRoof}. {\tt PyPVRoof} is a Python package to extract essential PV installation characteristics. These characteristics are tilt angle, azimuth, surface, localization, and installed capacity. {\tt PyPVRoof} is designed to cover all use cases regarding data availability and user needs and is based on a benchmark of the best existing methods. Data for replicating our accuracy benchmarks are available on our Zenodo repository \cite{tremenbert2023pypvroof}, and the package code is accessible at this URL: \url{https://github.com/gabrielkasmi/pypvroof}.Comment: 22 pages, 9 figures, 5 table

Analysis of the uncertainty in the estimates of regional PV power generation evaluated with the upscaling method

International audienceThe estimation of the regional photovoltaic (PV) power generation is an important step prior to the forecast of the PV power generation and its integration into the energy supply system. The large majority of PV plants not being measured in Germany, the total PV power generated in a region is commonly estimated by upscaling the power production of a set of reference PV plants to the entireness of the plants installed in the considered area. A given uncertainty can be expected in the estimation of the power generation of a PV plant with the upscaling method when the reference plants used have different configurations or weather conditions. To gain better insight into the performance of the upscaling method, its error has been analysed using power measurements of a set of 366 PV plants. The analysis allows an understanding of the mechanisms underlying the uncertainty of the upscaling method and quantifies its error for the test study considered. In the case study analysed, it could be shown that the quarter hourly RMSE 1 value decreases with an increasing number of reference plants and a decreasing number of un-metered plants. It could also be shown that even for a large number of reference plants, a variation of the RMSE between 0.01 and 0.025 kW/kWp can be observed, depending on the choice of the reference plants. It is shown that the average distance between a reference and unknown plant constitutes a good indicator of the performance of a set of reference plants, but that the match between the characteristics of the reference and unknown plant also plays an important role, which could not be quantified with the available dataset

An approach for the estimation of the aggregated photovoltaic power generated in several European countries from meteorological data

Classical approaches to the calculation of the photovoltaic (PV) power generated in a region from meteorological data require the knowledge of the detailed characteristics of the plants, which are most often not publicly available. An approach is proposed with the objective to obtain the best possible assessment of power generated in any region without having to collect detailed information on PV plants. The proposed approach is based on a model of PV plant coupled with a statistical distribution of the prominent characteristics of the configuration of the plant and is tested over Europe. The generated PV power is first calculated for each of the plant configurations frequently found in a given region and then aggregated taking into account the probability of occurrence of each configuration. A statistical distribution has been constructed from detailed information obtained for several thousands of PV plants representing approximately 2 % of the total number of PV plants in Germany and was then adapted to other European countries by taking into account changes in the optimal PV tilt angle as a function of the latitude and meteorological conditions. The model has been run with bias-adjusted ERA-interim data as meteorological inputs. The results have been compared to estimates of the total PV power generated in two countries: France and Germany, as provided by the corresponding transmission system operators. Relative RMSE of 4.2 and 3.8 % and relative biases of −2.4 and 0.1 % were found with three-hourly data for France and Germany. A validation against estimates of the country-wide PV-power generation provided by the ENTSO-E for 16 European countries has also been conducted. This evaluation is made difficult by the uncertainty on the installed capacity corresponding to the ENTSO-E data but it nevertheless allows demonstrating that the model output and TSO data are highly correlated in most countries. Given the simplicity of the proposed approach these results are very encouraging. The approach is particularly suited to climatic timescales, both historical and future climates, as demonstrated here

Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Knowledge of the spatial and temporal characteristics of solar surface irradiance (SSI) is critical in many domains. While meteorological ground stations can provide accurate measurements of SSI locally, they are sparsely distributed worldwide. SSI estimations derived from satellite imagery are thus crucial to gain a finer understanding of the solar resource. Inferring SSI from satellite images is, however, not straightforward, and it has been the focus of many researchers in the past 30 to 40 years. For long, the emphasis has been on models grounded in physical laws with, in some cases, simple statistical parametrizations. Recently, new satellite SSI retrieval methods have been emerging, which directly infer the SSI from the satellite images using machine learning. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Météo-France. The performance of the models is evaluated on a test dataset independent from the training set in both space and time and compared to that of a state-of-the-art physical retrieval model from the Copernicus Atmosphere Monitoring Service (CAMS). We found that the data-driven model's performance is very dependent on the training set. Provided the training set is sufficiently large and similar enough to the test set, even a simple MLP has a root mean square error (RMSE) that is 19 % lower than CAMS and outperforms the physical retrieval model at 96 % of the test stations. On the other hand, in certain configurations, the data-driven model can dramatically underperform even in stations located close to the training set: when geographical separation was enforced between the training and test set, the MLP-based model exhibited an RMSE that was 50 % to 100 % higher than that of CAMS in several locations.</p

Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales

We use a Bayesian method, optimal interpolation, to improve satellite derived irradiance estimates at city-scales using ground sensor data. Optimal interpolation requires error covariances in the satellite estimates and ground data, which define how information from the sensor locations is distributed across a large area. We describe three methods to choose such covariances, including a covariance parameterization that depends on the relative cloudiness between locations. Results are computed with ground data from 22 sensors over a 75×80 km area centered on Tucson, AZ, using two satellite derived irradiance models. The improvements in standard error metrics for both satellite models indicate that our approach is applicable to additional satellite derived irradiance models. We also show that optimal interpolation can nearly eliminate mean bias error and improve the root mean squared error by 50%

Climate proofing the renewable electricity deployment in Europe - Introducing climate variability in large energy systems models

Climate and weather conditions influence energy demand. as well as electricity generation, especially due to the strong development of renewable energy. The changes of the European energy mix, together with ongoing climate change, raise a number of questions on impact on the electricity sector. In this paper we present results for the whole of the European power sector regarding on how considering current and future climate variability affects the results of a TIMES energy system model for the whole European power sector (eTIMES-EU) up to 2050. For each member-state we consider six climate projections to generate future capacity factors for wind, solar and hydro power generation. as well as temperature impact on electricity demand for heating and cooling. These are input into the eTIMES-EU model to assess how climate affects the optimal operation of the power system and if current EU-wide RES and emissions target deployment may be affected. Results show that although at EU-wide level there are no substantial changes, there are significant differences in countries RES deployment (especially wind and solar) and in electricity trade.info:eu-repo/semantics/publishedVersio

Leitstudie 2010

Strategien zu erarbeiten, die aufzeigen, wie das langfristige Klimaschutzziel 2050 in Deutschland erreicht werden kann, ist das oberste Ziel von Studien, die seit gut einem Jahrzehnt vom DLR-ITT, Abteilung Systemanalyse und Technikbewertung mit wechselnden Projektpartnern für das BMU und das UBA durchgeführt werden. In der Leitstudie 2010 entstanden auf der Basis differenzierter und aktualisierter Potenzialabschätzungen, die technische, strukturelle und ökologische Kriterien berücksichtigen, und detaillierten Technik- und Kostenanalysen zu den Einzeltechnologien der Erneuerbaren verschiedene Szenarien ihres möglichen langfristigen Ausbaus in Wechselwirkung mit den übrigen Teilen der Energieversorgung in Deutschland. Für die Leitstudie 2010 haben die Projektpartner DLR, Stuttgart und Fraunhofer-IWES, Kassel erstmals mittels geeigneter Modelle eine vollständige dynamische und teilweise räumlich aufgegliederte Simulation der Stromversorgung durchgeführt. Außerdem wird der Untersuchungsraum für diese Simulation auf ganz Europa (einschließlich einiger nordafrikanischer Länder) ausgedehnt, um die Wechselwirkungen eines nationalen Umbaus der Energieversorgung mit der Entwicklung in Nachbarregionen erfassen zu können