Making use of a specifically designed SW tool, the authors here presents the results of an activity for the evaluation of energy consumption of buses for urban applications. Both conventional and innovative transport means are considered to obtain interesting comparative conclusions. The SW tool simulates the dynamical behaviour of the vehicles on really measured paths making it possible to evaluate their energetic performances on a Tank to Wheel (TTW) basis. Those data, on such a wide and comparable range were still unavailable in literature.

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251 Views

The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.

 

Instructions: 

This dataset contain a PV Arrays Models Pack with some models of PV Solar Arrays carried out in Matlab and Simulink. The PV Models are grouped in three ZIP files which correspond to the papers listed above.

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7830 Views

The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.

Instructions: 

1. Open the "Banu_power_PVarray_grid_EPE2014_.slx" file with Matlab R2014a 64 bit version or a newer Matlab release. 2. To simulate various grid faults on PV System see the settings of the "Fault" variant subsystem block (Banu_power_PVarray_grid_EPE2014_/20kV Utility Grid/Fault) in Model Properties (File -> Model Properties -> Model Properties -> Callbacks -> PreLoadFcn* (Model pre-load function)):           MPPT_IncCond=Simulink.Variant('MPPT_MODE==1')           MPPT_PandO=Simulink.Variant('MPPT_MODE==2')           MPPT_IncCond_IR=Simulink.Variant('MPPT_MODE==3')           MPPT_MODE=1           Without_FAULT=Simulink.Variant('FAULT_MODE==1')           Single_phases_FAULT=Simulink.Variant('FAULT_MODE==2')           Double_phases_FAULT=Simulink.Variant('FAULT_MODE==3')           Double_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==4')           Three_phases_FAULT=Simulink.Variant('FAULT_MODE==5')           Three_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==6')           FAULT_MODE=1 3. For more details about the Variant Subsystems see the Matlab Documentation Center: https://www.mathworks.com/help/simulink/variant-systems.html or https://www.mathworks.com/help/simulink/examples/variant-subsystems.html

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4896 Views

The dataset is supplementary material for the research article 'Techno-economic assessment of grid-level battery energy storage supporting distributed photovoltaic power' published in IEEE Access in October 2021. The dataset corresponds to the annual timeseries at 1-minute resolution (525,600 steps) of the per-unit profiles used for the electric load and the per-unit power output of 8 PV systems.

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73 Views

There is an industry gap for publicly available electric utility infrastructure imagery.  The Electric Power Research Institute (EPRI) is filling this gap to support public and private sector AI innovation.  This dataset consists of ~30,000 images of overhead Distribution infrastructure.  These images have been anonymized, reviewed, and .exif image-data scrubbed.  These images are unlabeled and do not contain annotations.  EPRI intends to label these data to support its own research activities.  As these labels are created, EPRI will periodically update this dataset with those data.

Instructions: 

These images are not labeled or annotated.  However, as these images are labeled, EPRI will update this dataset periodically.  If you have annotations you'd like to contribute, please send them, with a description of your labeling approach, to ai@epri.com.

 

Also, if you see anything in the imagery that looks concerning, please send the image and image number ai@epri.com

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152 Views

The database configuration is based on a joint work of the system planning company (representing the Brazilian Energy Ministry) and private companies (market players and consulting companies) to study the impact of the large integration of renewables in the Brazilian system.

This dataset was used to produce the results of following paper:

Title: An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem

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91 Views

The heating and electricity consumption data are the results of an energy audit program aggregated for multiple load profiles of a residential customer. These profiles include HVAC systems loads, convenience power, elevator, etc. The datasets are gathered between December 2010 and November 2018 with a one-hour timestep resolution, thereby containing 140,160 measurements, half of which is for heat or electricity. In addition to the historical energy consumption values, a concatenation of weather variables is also available.

Instructions: 

This is a publicly available dataset of heating and electricity consumption profiles, aggregated from multiple load profiles of a residential customer. The dataset is gathered between December 2010 and November 2018 with a one-hour time step resolution, thereby containing 70,080 measurements. In addition to the historical energy consumption values, a concatenation of meteorological variables is also included. The weather variables are air pressure, temperature, and humidity plus wind speed and solar irradiation at the predetermined location. 

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900 Views

Unit commitment and system data used in the following research paper:

G. Gutiérrez-Alcaraz, B. Díaz-López, J. M. Arroyo, and V. H. Hinojosa, “Large-scale preventive security-constrained unit commitment considering N-k line outages and transmission losses: System data.”

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72 Views

This Matlab model and the included results are submitted as reference for the paper ''. 

Presenting a comparative study of the Sequential Unscented Kalman Filter (SUKF), Least-squares (LS) Multilateration and standard Unscented Kalman Filter (UKF) for localisation that relies on sequentially received datasets. 

The KEWLS and KKF approach presents a novel solution using Linear Kalman Filters (LKF) to extrapolate individual sensor measurements to a synchronous point in time for use in LS Multilateration. 

 

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29 Views

We generated attack datasets 1 based on real data from Austin, Texas.

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170 Views

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