Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments

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

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

Aerosols attenuating the solar radiation collected by solar tower plants: the horizontal pathway at surface level [Póster]

Póster elaborado para el Solar Paces 2015 celebrado del 12 al 16 de octubre de 2015 en Cape Town (Sudáfrica

Aerosols attenuating the solar radiation collected by solar tower plants: the horizontal pathway at surface level

Aerosols attenuate the solar radiation collected by solar tower plants (STP), along two pathways: 1) the atmospheric column pathway, between the top of the atmosphere and the heliostats, resulting in Direct Normal Irradiance (DNI) changes; 2) the grazing pathway close to surface level, between the heliostats and the optical receiver

Solar energy attenuated in a Solar Thermal Energy plant, due to aerosol extinction between the heliostats and the optical receiver

Presentación realizada en: 15th EMS Annual Meeting and 12th European Conference on Applications of Meteorology (ECAM) celebrado del 7 al 11 de septiembre de 2015 en Sofía, Bulgaria

XMM-Newton Observations of the Ultraluminous Nuclear X-ray Source in M33

We present observations with XMM-Newton of M33 X-8, the ultraluminous X-ray source (L_{0.5-10 keV} ~ 2x10^39 erg/s) closest to the centre of the galaxy. The best-fit model is similar to the typical model of Galactic black holes in very high state. Comparison with previous observations indicates that the source is still in a very high state after about 20 years of observations. No state transition has been observed even during the present set of XMM-Newton observations. We estimate the lower limit of the mass of the black hole >6 M_sun, but with proper parameters taking into account different effects, the best estimate becomes 12 M_sun. Our analysis favours the hypothesis that M33 X-8 is a stellar mass black hole candidate, in agreement with the findings of other authors. In addition, we propose a different model where the high luminosity of the source is likely to be due to orientation effects of the accretion disc and anisotropies in the Comptonized emission.Comment: 9 pages, 5 figures. Accepted for publication on A&A Main Journal. A bug in the introduction has been corrected (a citation

E4F1 deficiency results in oxidative stress–mediated cell death of leukemic cells

Deletion of E4F1 inflicts mitochondrial damage and oxidative stress on murine and human myeloid leukemia cells but not healthy macrophages

Towards a future-proof climate database for European energy system studies

In 2013, the European Network of Transmission System Operators (TSOs) for electricity (ENTSO-E) created the Pan-European Climate Database (PECD), a tool that has underpinned most studies conducted by TSOs ever since. So far, the different versions of the PECD have used so-called modern-era ‘reanalysis’ products that represent a gridded amalgamation of historical conditions from observations. However, scientific evidence suggests, and recent European regulation requires, that power system adequacy studies should take climate change into account when estimating the future potential of variable renewable resources, such as wind, solar and hydro, and the impact of temperature on electricity demand. This paper explains the need for future climate data in energy systems studies and provides high-level recommendations for building a future-proof reference climate dataset for TSOs, not just in Europe, but also globally

Towards a future-proof climate database for European energy system studies

In 2013, the European Network of Transmission System Operators (TSOs) for electricity (ENTSO-E) created the Pan-European Climate Database (PECD), a tool that has underpinned most studies conducted by TSOs ever since. So far, the different versions of the PECD have used so-called modern-era 'reanalysis' products that represent a gridded amalgamation of historical conditions from observations. However, scientific evidence suggests, and recent European regulation requires, that power system adequacy studies should take climate change into account when estimating the future potential of variable renewable resources, such as wind, solar and hydro, and the impact of temperature on electricity demand. This paper explains the need for future climate data in energy systems studies and provides high-level recommendations for building a future-proof reference climate dataset for TSOs, not just in Europe, but also globally