• Energy Research
• 2021-2025close
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This code is in support of my research paper 'Design and Analysis of 2kW Residential PEM Fuel Cell Inverter'. This code uses data uploaded on IEEE DataPort, titled 'Data:2kW Hydrogen Residential Inverter', and on Mendeley titled 'Data:2KW PEM Fuel Cell Residential Inverter'.For more details,pls read ReadMe.Txt.

de-climate-change-analysis provides statistical analysis and plotting functions to determine absolute and relative changes in climate variables. It is used by the Digital Earth Climate Change Backend Module as part of the Digital Earth Flood Event Explorer. It is developed at the Helmholtz-Zentrum Hereon (https://www.hereon.de) in collaboration with the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). We acknowledge funding from the Initiative and Networking Fund of the Helmholtz Association through the project "Digital Earth". {"references": ["Gitlab Repository: https://git.geomar.de/digital-earth/de-climate-change/de-climate-change-analysis"]}

The I-V curves of solar photovoltaic (PV) systems have been widely used to determine their health status. Usually, the I-V curve is described as a function of the cardinal points: the short-circuit point $\left(0, I_\mathrm{sc}\right)$, the open-circuit point $\left(V_\mathrm{oc},0\right)$ and the maximum power point $\left(V_\mathrm{mp},I_\mathrm{mp} \right)$. In this context, to study the I-V curves the single diode model (SDM) is often used. In this paper, we present a practical approach for determining the parameters domain of the SDM given a set of cardinal points. An in-depth mathematical analysis proved that the cardinal points must satisfy $V_\mathrm{oc}/2

The files contained within this dataset describe a simulation tool that predicts the energy consumption of a battery electric vehicle. The tool is written in MATLAB-code and is connected to various API's to make use of up-to-date route information (Overpass OpenStreetMap API), height information (SRTM elevation map), and weather information (OpenWeatherMap API). The prediction method relies on a physics-based interpretation of the energy consumption of the vehicle. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. In the supplied version of this tool, the parameters represent the Voltia eVan, which is a fully electric delivery van with a swappable traction battery. The tool was developed within the Dynamics & Control research-group at Eindhoven University of Technology. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 713771 (EVERLASTING).Version: 0.1.3 Date: 2021-08-23Change Log Version 0.1.3------------------------- Updated 'readhght' from Fran��ois Beauducel, which again omits the use of the NASA/EarthDATA account that was introduced in the previous version. SRTM elevation data can now again be obtained without account.Change Log Version 0.1.2------------------------- Changed the inputs to 'readhght' to use the NASA/EarthDATA server, because the original server hosting the SRTM DEM appears to be off-line. This new server requires a NASA/EarthDATA account. If road slope is to be included, 'settings.includeSlope' (l. 76, TUe_MECPRO.m) should be set to 'true' and NASA/EarthDATA account credentials should be entered in 'NASA_Earthdata_Login.txt'.- Corrected an error in velocityProfilePredicion.m where the prescribed maximum vehicle velocity was wrongly considered to be [m/s] instead of [km/h].- Corrected an error in processOSMmap.m that resulted in the incorrect registration of the maximum legislated velocity, in case it is prescribed as 'none' (German Highways). Now, 200km/h is assumed in case the maximum legislated velocity is 'none'.

Increasing levels of anthropogenic underwater noise have caused concern over their potential impacts on marine life. Offshore renewable energy developments and seismic exploration can produce impulsive noise which is especially hazardous for marine mammals because it can induce auditory damage at shorter distances and behavioural disturbance at longer distances. However, far-field effects of impulsive noise remain poorly understood, causing a high level of uncertainty when predicting the impacts of offshore energy developments on marine mammal populations. Here we used a 10-year dataset on the occurrence of coastal bottlenose dolphins over the period 2009-2019 to investigate far-field effects of impulsive noise from offshore activities undertaken in three different years. Activities included a 2D seismic survey and the pile installation at two offshore wind farms, 20-75 km from coastal waters known to be frequented by dolphins. We collected passive acoustic data in key coastal areas and used a Before-After Control-Impact design to investigate variation in dolphin detections in areas exposed to different levels of impulsive noise from these offshore activities. We compared dolphin detections at two temporal scales, comparing years and days with and without impulsive noise. Passive acoustic data confirmed that dolphins continued to use the impact area throughout each offshore activity period, but also provided evidence of short-term behavioural responses in this area. Unexpectedly, and only at the smallest temporal scale, a consistent increase in dolphin detections was observed at the impact sites during activities generating impulsive noise. We suggest that this increase in dolphin detections could be explained by changes in vocalization behaviour. Marine mammal protection policies focus on the near-field effects of impulsive noise; however, our results emphasize the importance of investigating the far-field effects of anthropogenic disturbances to better understand the impacts of human activities on marine mammal populations. Echolocation detectors (CPODs; Chelonia Ltd) were deployed between 2009 and 2019 to investigate the variation in dolphin detections in relation to the impulsive noise from three energy developments: a seismic survey for oil and gas exploration and the installation of foundation piles for two offshore wind farms (Beatrice Offshore Wind Farm and Moray East Offshore Wind Farm). Data on the timing of the seismic survey and piling operations were provided by the developers (Oil and Gas UK Ltd., COWRIE, Beatrice Offshore Wind Ltd. and Moray Offshore Wind Farm East). Data consist of 7 files and include the datasets and R code required to repeat all the analyses. A full description of the files provided in the Readme.txt file: OFB_FarField_DPH.csv OFB_FarField_BOWL.csv OFB_FarField_MEOW.csv OFB_FarField_BACI_Obtain_DPH_Dataset.R OFB_FarField_DPH_for_BACI.csv OFB_FarField_BACI_DPH_Models.R OFB_FarField_Readme.txt

This repository will contain the code for the paper:Veviurko, G.; Böhmer, W.; Mackay L.; de Weerdt, M. Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability, Energies 2022.

Energy-saving matrix decompositions on CPU-GPU Heterogeneous Systems

Investigation of model biases in historical internal variability using explainable AI If you use this software, please cite it as below.

Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early-Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution, have included an over reliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Using process-explicit macroecological models that integrate modern and fossil occurrence records, spatiotemporal reconstructions of past climatic change, speciesspecific population ecology and the growth and spread of anatomically modern humans, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia, and in its expansion in North America. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years requires high dispersal abilities, large maximum densities, and a small Allee effect. Climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses reveal that the range collapse and extinction of the muskox in Europe (~ 13 thousand years ago) was caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene-Holocene transition signals a vulnerability of this species to future increased warming. By disentangling the ecological processes that shaped the distribution of the muskox through space and time, process-explicit models have important applications for the future conservation and management of this iconic species in a warming Arctic. We built process-explicit macroecological models of muskox that simulate interactions between metapopulation dynamics, climate variability, and hunting by humans. We used calibrated fossils and modern occurrence records obtained from publicly available databases and published literature. Records were intersected with paleoclimate reconstructions accessed using PaleoView, and modern climate data from CRU TS v4. Niche hypervolumes and spatiotemporal projections of habitat suitability were built in R using the 'hypervolume' package. Process-explicit macroecological models were built in R using the 'poems' and 'paleopop' package. Human abundance was modelled using a Climate Informed Spatial Genetics Model (CISGeM). Funding provided by: Australian Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000923Award Number: DP180102392Funding provided by: Australian Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000923Award Number: FT140101192Funding provided by: Danish Research Foundation*Crossref Funder Registry ID: Award Number: DNRF96