We present photographs of inspections of the electrical power network (13.8 kV) carried out in southern Brazil. The inspections were performed close to the city of Blumenau by researchers from the high voltage laboratory, Regional University of Blumenau (FURB).

240 photographs of the electrical power network are presented:120 are of damaged structures and/or components.120 are in normal conditions.


The 2 PHASE ENERGY METER 100A (2PEM-100A) is a power consumption monitor based on an embedded system. In the hope of promoting responsible energy consumption, we have decided to release this open source hardware project that was developed in the course "Development of Electronic Prototypes" at the ESPOL University. The 2PEM-100A allows to monitor the following parameters: Voltage (V), Current (A), Power (W), Frequency (Hz), Energy (KWh), Power Factor and Temperature (°C) of the 2PEM-100A.

Last Updated On: 
Thu, 12/09/2021 - 03:02
Citation Author(s): 
Adrian Bazurto, Víctor Asanza, Ronald Reyes,Douglas Plaza, Diego Peluffo-Ordóñez

The problem of cooling in rescue robots is similar to that of the entire domain of product development involving electronic systems. When considering mission-oriented rescue robots, this issue becomes more severe, as the tolerance to failure is remarkably low. While cooling is considered indispensable, the hazardous environmental condition of the scene of deployment, comprising of water, dust, toxic gases, or fire, constrains the choices of the method.


This is a unique energy-aware navigation dataset collected at the Canadian Space Agency’s Mars Emulation Terrain (MET) in Saint-Hubert, Quebec, Canada. It consists of raw and post-processed sensor measurements collected by our rover in addition to georeferenced aerial maps of the MET (colour mosaic, elevation model, slope and aspect maps). The data are available for download in human-readable format and rosbag (.bag) format. Python data fetching and plotting scripts and ROS-based visualization tools are also provided.


The entire dataset is separated into six different runs, each covering different sections of the MET at different times. The data was collected on September 4, 2018 between 17:00 and 19:00 (Eastern Daylight Time). The data is available in both human-readable format and in rosbag (.bag) format.

To avoid extremely large files, the rosbag data of every run was broken down into two parts: “runX_clouds_only.bag” and “runX_base.bag”. The former only contains the point clouds generated from the omnidirectional camera raw images after data collection, and the latter contains all the raw data and the remainder of the post-processed data. Both rosbags possess consistent timestamps and can be merged together using bagedit for example. A similar breakdown was followed for the human-readable data.

Aside from point clouds, the post-processed data of every run includes a blended cylindrical panorama made from the omnidirectional sensor images, planar rover velocity estimates from wheel encoder data and an estimated global trajectory obtained by fusing GPS and stereo imagery coming from cameras 0 and 1 of the omnidirectional sensor using VINS-Fusion later combined with the raw IMU data. Global sun vectors and relative ones (with respect to the rover’s base frame) were also calculated using the Pysolar library. This library also provided clear-sky direct irradiance estimates along every pyranometer measurement collected. Lastly, the set of georeferenced aerial maps, the transforms between different rover and sensor frames, and the intrinsic parameters of each camera are also available.

We strongly recommend interested users to visit the project's home page, which provides additional information about each run (such as their physical length and duration). All download links on the home page were updated to pull from the IEEE DataPort servers. A more detailed description of the test environment and hardware configuration are provided in the project's official journal publication.

Once the data products of the desired run are downloaded, the project's Github repository provides a lightweight ROS package and python utilities to fetch the desired data streams from the rosbags.


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.


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.


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


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


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.


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. 


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.”


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.