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Author: Daniel Vázquez Pombo (dvapo@elektro.dtu.dk) ------------------------------------------------------------------------------- This dataset corresponds to the results of the paper titled: "Multi-Horizon Data-Driven Wind Power Forecast: From Nowcast to 2 Days-Ahead" 4th International Conference on Smart Energy Systems and Technologies (SEST) - 2021 -> https://sites.univaasa.fi/sest2021/ Submmited: Dec 2020 Accepted: Feb 2021 Published: Sep 2021 ------------------------------------------------------------------------------- The folder contains all the results presented in the paper, for clarity. Additional resources might be supplied under request. -------------------------------------------------------------------------------

This is the ERA5 dataset that can be used to calculate air density for wind energy purposes using the equations presented in https://doi.org/10.3390/en12112038. The NetCDF file contains mean fields of monthly means from 2010 to 2020 of the variable temperature, surface pressure, specific humidity and the lapse rate. The horizontal grid resolution is 0.25 degrees and covers the whole globe. These fields are used in the WAsP software version 12.6 and above to calculate the air density at specified heights above mean sea level. The WAsP software (www.wasp.dk) is the industry standard method to calculate the annual energy production of wind farms. The ERA5 data are generated using Copernicus Climate Change Service information [2020]

Supporting data for publication 'Performance analysis of a high-efficiency multi-bed active magnetic regenerator device" submitted to the Applied Thermal Engineering (DOI 10.1016/j.applthermaleng.2021.117569)The Excel sheet summarizes the experimental output parameters for the performance data presented in the publication. All data were measured continuously after reaching steady-state conditions, and the data were averaged over a time span of 600 s.

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

The supporting information of the journal article "Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment" from Fabbri et al. (2022) includes one file with the following content: S1 Details of consequential modelling: feedstock S1.1 Identification type of changes (demand or supply) S1.2 Identification of constrains in the market S1.3 Identification of product substitutions S1.4 Identification of affected production technology S1.5 Identification of marginal crop and marginal supplier S2 Details of consequential modelling: by-products S3 Model parameters and unit processes S3.1 Sugar beet S3.2 Sugar cane S3.3 Wheat S3.4 Maize S3.5 Wood S3.6 Residual woodchips and sawdust S4 Review of land use change accounting methods S4.1 Direct land use change (dLUC) S4.2. Indirect land use change (iLUC) S5 Additional results S5.1 Influence of spatial differentiation in LCIA S5.2 Influence of indirect land use change (iLUC) S6 References This work was funded by the Innovation Fund Denmark under the Grand Solutions instrument; project ReMEG "Renewable Mono Ethylene Glycol for PET Plastic".

Abstract The vortex-induced forces on an extruded cylinder with a span of two diameters representative of a finite segment of a non-tapered wind turbine tower at a very high Reynolds number (Re = 8.0×106) are numerically investigated using an incompressible Navier-Stokes flow solver with an Improved Delayed Detached Eddy Simulation (IDDES) turbulence model and correlation-based boundary layer transition modelling. The solution shows spanwise correlated structured vortex shedding with the Strouhal number St = 0.48. The boundary layer transition is found to occur at θ transition = 70 ◦ , and boundary layer separation is found to occur at θ separation = 120 ◦ . Results from the grid dependency study strongly imply that when using IDDES, the Strouhal number converges to higher values than previously reported by the literature as the grid is refined, with results ranging from St ∼ 0.44 using a grid with 4.2 × 106 cells, to St = 0.48 for the finest considered grid with 33 × 106 cells. This behaviour is not seen for URANS, where St = 0.33 for the finest grid.

Increasing the efficiency and flexibility of electricity demand is necessary for ensuring a cost-effective and reliable transition to zero-carbon electricity systems. Such demand-side management (DSM) resources have been procured by utilities for decades via energy efficiency and demand response programs; however, the key drivers of program enrollment and customer participation levels remain poorly understood --- even as governments and grid planners seek to scale up the deployment of DSM assets to meet climate targets. Here we systematically review the evidence on multiple factors that may influence customer enrollment and participation in building DSM programs, focusing primarily on residential and commercial buildings. We examine the contexts in which relationships between DSM factors and outcomes are most often explored and with which methods; we also score the strength, direction, and internal consistency of each factor's reported impact on the enrollment and participation outcomes. We find that studies most commonly assess the effects of economic incentives for load flexibility on program participation levels, often using simulation-based methods in lieu of measured data. Few studies focus on program enrollment outcomes or regulatory drivers of either enrollment or participation, and gaps are also evident in the coverage of emerging DSM opportunities like load electrification. Removal of structural barriers (e.g., the lack of controls infrastructure) and the use of third party services (e.g., load aggregators) are the factors with the largest positive impacts on DSM outcomes, but no single factor emerges as clearly most impactful. For a given factor, the range of reported impacts typically varies widely across the relevant studies reviewed. Our findings provide a snapshot of the state of knowledge about building DSM and customer decision-making, and they expose key gaps in understanding that must be filled if building DSM is to expand as a critical resource for operating clean power grids. Files in this record: - "ENB-D-24-00887 Data.xlsx" (search strings, screened paper citations, scored paper citations, scoring template, scoring categories, and final scoring data for the article.)

Supporting data for publication 'Impact of hysteresis on caloric cooling performance" submitted in the International Journal of Refrigeration (DOI 10.1016/j.ijrefrig.2020.10.012) The data comprise:1) An Excel file presenting the modeling results from the 1D regenerator model with hysteresis term (Qhys) to predict how modeled materials with a range of hysteresis values affect the cooling performance, including COP and cooling performance (Pcool). The Excel file presents the results for the six model materials, which have different physical (and thermodynamic) properties, i.e., specific heat capacity at zero field (c) and isothermal entropy change (s). For example, the material 'LoCLoS' has both a low specific heat capacitiy and a low isothermal entropy change. 2) An MS Word document with the additional Figs. S1 and S2 showing the modeled COPs for five model materials for a design cooling power of 50 W as a function of cycle frequency for different hysteresis values. The document also comprises a Table S1 presenting the modeled COPs for a design cooling power of 300 W as a function of cycle frequency for different hysteresis values.