Linear scaling relations have led to an understanding of trends in catalytic activity and selectivity of many reactions in heterogeneous and electro-catalysis. Yet, linear scaling between the chemisorption energies of any two small molecule adsorbates is not guaranteed. A prominent example is the lack of scaling between the chemisorption energies of carbon and oxygen on transition metal surfaces. In this work, we show that this lack of scaling originates from different re-normalised adsorbate valence energies of lower-lying oxygen versus higher-lying carbon. We develop a model for chemisorption of small molecule adsorbates within the d-band model by combining a modified form of the Newns-Anderson hybridisation energy with an effective orthogonalization term. We develop a general descriptor to a priori determine if two adsorbates are likely to scale with each other. This record contains the AiiDA archive required to reproduce all calculations in the manuscript.

For closed-shell molecules, valence electron binding energies may be calculated accurately and efficiently with ab initio electron-propagator methods that have surpassed their predecessors. Advantageous combinations of accuracy and efficiency range from cubically scaling methods with mean errors of 0.2 eV to quintically scaling methods with mean errors of 0.1 eV or less. The diagonal self-energy approximation in the canonical Hartree–Fock basis is responsible for the enhanced efficiency of several methods. This work examines the predictive capabilities of diagonal self-energy approximations when they are generalized to the canonical spin–orbital basis of unrestricted Hartree–Fock (UHF) theory. Experimental data on atomic electron binding energies and high-level, correlated calculations in a fixed basis for a set of open-shell molecules constitute standards of comparison. A review of the underlying theory and analysis of numerical errors lead to several recommendations for the calculation of electron binding energies: (1) In calculations that employ the diagonal self-energy approximation, Koopmans’s identity for UHF must be qualitatively correct. (2) Closed-shell reference states are preferable to open-shell reference states in calculations of molecular ionization energies and electron affinities. (3) For molecular electron binding energies between doublets and triplets, calculations of electron detachment energies are more accurate and efficient than calculations of electron attachment energies. When these recommendations are followed, mean absolute errors increase by approximately 0.05 eV with respect to their counterparts obtained with closed-shell reference orbitals.

In 2023, around 44 percent of electricity from renewable energies in Germany was generated by onshore wind power. Around 23 percent was generated by photovoltaics.

It has been known for decades that high performances for explosives (as characterized by detonation velocity D, detonation pressure P, or Gurney energy EG) are connected with high impact sensitivities, i.e., low values of the drop weight impact height h50. This trade-off is theoretically substantiated for the first time. It stems from the primary role of the amount of chemical energy evolved per atom for both performance and sensitivity. Under realistic assumptions, log(h50) increases linearly with D–4 or equivalently with P–2 or EG–1. This prediction proves consistent with experimental data for nonaromatic nitro compounds. The occurrence of different explosophores on the same molecule is suggested as a factor influencing the performance-sensitivity trade-off. Finally, it is shown that a large body of data may be explained by the present approach, which naturally integrates thermodynamic (energy content) as well as kinetic (activation energies) aspects. This model should help in designing powerful high energy compounds with acceptable sensitivity.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.This dataset includes electricity and natural gas usage data reported at the city, town, and village level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the county and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-Community-Energy-U/4txm-py4p.Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

Information on the number and capacity of green electricity production facilities in the Brussels-Capital Region. The three technologies present in the Brussels-Capital Region are Solar, Cogeneration and Steam Turbines coupled up to the Incinerator of the Brussels-Capital Region. Cogeneration installations are powered by three fuels: natural gas, biogas and liquid biomass in the form of rapeseed oil. The data in the reports related to the installations is broken down by type of owner (public company, private company or private individual), by municipality, technology, energy source and power category (expressed in [MW]). It is important to note that installations already commissioned before the date on which these reports were updated will be registered with BRUGEL at a later date. Regarding the Green Certificates, the reports show the number of GC issued, the stock, the number of concluded transactions, the number of GC sold, the simple and weighted average prices of the GC as well as the total value of the transactions in the quarters of the different quota return periods. A segmentation of transactions according to the simple average price is also presented. For the Guarantees of Origin, the reports show the number of GO subject to transactions in RBC, namely inter-regional transfers, imports and exports as well as the geographical origin and the different sources of renewable energy consumed in Brussels per year. Data is updated on a monthly basis.

Indicateur reposant sur le bilan énergétique du territoire réalisé chaque année. Il prend en compte la consommation d des différents secteurs (résidentiel, tertiaire, etc.) et des différents vecteurs (électricité, gaz, etc.). Les données énergétiques sont issues du Service des données et études statistiques (SDES) pour l’électricité et le gaz naturel et des Rapports annuels pour le réseau de chaleur et de froid, ainsi que des données indirectes pour le fioul et la biomasse.

In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions. The record includes all molecular-dynamics trajectories, energies and forces used to obtain the solvation energies of alkaline and alkaline-earth ions in water, as reported in Table 2 of referenced paper.

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

Ratio Energies Lp - 当前值，历史数据，预测，统计，图表和经济日历 - Mar 2025.Data for Ratio Energies Lp including historical, tables and charts were last updated by Trading Economics this last March in 2025.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities and CCA administrators under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported by utilities at the county level. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level. Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Hot electrons generated by laser-plasma instabilities degrade the performance of laser-fusion implosions by preheating the DT fuel and reducing core compression. The hot-electron energy deposition in the DT fuel has been directly measured for the first time by comparing the hard x-ray signals between DT-layered and mass-equivalent ablator-only implosions. The electron energy deposition profile in the fuel is inferred through dedicated experiments using Cu-doped payloads of varying thickness. The measured preheat energy accurately explains the areal-density degradation observed in many OMEGA implosions. This technique can be used to assess the viability of the direct-drive approach to laser fusion with respect to the scaling of hot-electron preheat with laser energy.

Displays several units of energy consumption for households, businesses, and industries in the City of Chicago during 2010. Electric The data was aggregated from ComEd and Peoples Natural Gas by Accenture. Electrical and gas usage data comprises 88 percent of Chicago's buildings in 2010. The electricity data comprises 68 percent of overall electrical usage in the city while gas data comprises 81 percent of all gas consumption in Chicago for 2010. Census blocks with less than 4 accounts is displayed at the Community Area without further geographic identifiers. This dataset also contains selected variables describing selected characteristics of the Census block population, physical housing, and occupancy.

Datasets in digital electronic formats are provided for data contained in the publication "Recommended Values for the Gas Phase Enthalpies of Formation of Hydrogen-Oxygen Species;" J. Res. Natl. Inst. Stand. Technol. 121, 108-138 (2016); DOI: 10.6028/jres.121.005.In this work, we compiled gas phase enthalpies of formation for nine hydrogen-oxygen species (HxOy) and selected values for use. The compilation consists of values derived from experimental measurements, quantum chemical calculations, and evaluations. This work updates the recommended values in the NIST-JANAF (1985) and Gurvich et al (1989) thermochemical tables for seven species. For two species, HO3 and H2O3 (important in atmospheric chemistry) and not found in prior thermochemical evaluations, we also provide tables of thermochemical functions (Cp, S°, H°, and ΔfH°) as a function of temperature. In this work, we also provide supplementary data for the species consisting of zero point energies, vibrational frequencies, and ion reaction energetics.

As port clusters continue to evolve as critical hubs for global trade, there is an increasing emphasis on sustainability and operational efficiency. The integration of advanced energy systems, including electrified and hydrogen-powered container logistics, is essential for enhancing port operations while minimizing environmental impact. This dataset provides comprehensive parameters and data for integrated energy and container logistics systems within a port cluster, including detailed configuration information on energy units and logistics facilities. It reflects the current development of these technologies and supports research focused on optimizing coordination, management, and resource integration to enhance the efficiency and sustainability of modern port clusters.

This dataset contains three archives. The first archive, full_dataset.zip, contains geometries and free energies for nearly 44,000 solute molecules with almost 9 million conformers, in 42 different solvents. The geometries and gas phase free energies are computed using density functional theory (DFT). The solvation free energy for each conformer is computed using COSMO-RS and the solution free energies are computed using the sum of the gas phase free energies and the solvation free energies. The geometries for each solute conformer are provided as ASE_atoms_objects within a pandas DataFrame, found in the compressed file dft coords.pkl.gz within full_dataset.zip. The gas-phase energies, solvation free energies, and solution free energies are also provided as a pandas DataFrame in the compressed file free_energy.pkl.gz within full_dataset.zip. Ten example data splits for both random and scaffold split types are also provided in the ZIP archive for training models. Scaffold split index 0 is used to generate results in the corresponding publication.

The second archive, refined_conf_search.zip, contains geometries and free energies for a representative sample of 28 solute molecules from the full dataset that were subject to a refined conformer search and thus had more conformers located. The format of the data is identical to full_dataset.zip.

The third archive contains one folder for each solvent for which we have provided free energies in full_dataset.zip. Each folder contains the .cosmo file for every solvent conformer used in the COSMOtherm calculations, a dummy input file for the COSMOtherm calculations, and a CSV file that contains the electronic energy of each solvent conformer that needs to be substituted for "EH_Line" in the dummy input file.

This dataset includes the following resources:

— Renewable generation of electrical energy by process type — Consumption of biofuels in road transport — Net heat production by plant type — Quantities of biogas injected into the natural gas distribution network — Maximum net power of heating plants (in KWth)

— Automatically synchronised from LUSTAT database

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects.

BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

PUDL v2024.10.0 Data Release

This is a special early release to publish the new VCE Resource Adequacy Renewable Energy (RARE) dataset. It also includes final releases of EIA 860 and 923 data for 2023 and the FERC Form 714 data for 2021-2023, which had previously been integrated from the XBRL data published by FERC. See the release notes for more narrative detail.

New Data

Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset

• Integrate the VCE hourly capacity factor data for solar PV, onshore wind, and offshore wind from 2019 through 2023. The data in this table were produced by Vibrant Clean Energy, and are licensed to the public under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). This data complements the WECC-wide GridPath RA Toolkit data currently incorporated into PUDL, providing capacity factor data nation-wide with a different set of modeling assumptions and a different granularity for the aggregation of outputs. See GridPath Resource Adequacy Toolkit Data and Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset for more information. See #3872.

New Data Coverage

EIA 860

• Added EIA 860 final release data from 2023. See #3684 and PR #3871.

EIA 923

• Added EIA 923 final release data from 2023 and revised data from 2022. See #3902 and PR #3903.

FERC Form 714

• Integrated 2021-2023 years of the FERC Form 714 data. FERC updated its reporting format for 2021 from a CSV files to XBRL files. This update integrates the two raw data sources and extends the data coverage through 2023. See #3809 and #3842.

Schema Changes

• Added out_eia_yearly_assn_plant_parts_plant_gen table. This table associates records from the out_eia_yearly_plant_parts with plant_gen records from that same plant parts table. See issue #3773 and PR #3774.

Bug Fixes

• Included more retiring generators in the net generation and fuel consumption allocation. Thanks to @grgmiller for this contirbution #3690.

• Fixed a bug found in the rolling averages used to impute missing values in fuel_cost_per_mmbtu and to calculate capex_annual_addition_rolling. Thanks to RMI for identifying this bug! See issue #3889 and PR #3892.

Major Dependency Updates

• Updated to use Numpy v2.0 and Splink v4.0. See issues #3736, #3735 and PRs #3547, #3834.

Quality of Life Improvements

• We now use an asset factory to generate Dagster assets for near-identical FERC1 output tables. See #3147 and #3883. Thanks to @hfireborn and @denimalpaca for their work on this one!

Other PUDL v2024.10.0 Resources

• PUDL v2024.10.0 Data Dictionary
• PUDL v2024.10.0 Documentation
• PUDL in the AWS Open Data Registry
• PUDL v2024.10.0 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2024.10.0/
• PUDL v2024.10.0 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2024.10.0/
• Zenodo archive of the PUDL GitHub repo for this release
• PUDL v2024.10.0 release on GitHub
• PUDL v2024.10.0 package in the Python Package Index (PyPI)

Contact Us

If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. Here's a bunch of different ways to get in touch:

• Follow us on GitHub
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