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

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 The dataset consists of results of 10 different tests conducted on 8 different types of battery cells. All batteries are designed in 18650, but they differ in manufacturer and composition. The measurements can be divided into three groups: constant load tests, WLTP tests at different temperatures and transient tests. Constant load tests are performed at 0.1C and 1C as a function of battery capacity, both during charging and discharging. During transient tests, a 360s load/charge is followed by a 60s relaxation.

Instructions: 

Please see the attached: Complex_battery_test_18650.docx.

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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.

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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.

Instructions: 

Dataset "teplo.csv" is a simple text file. Each heating meter forms one daily record. The dataset has been collected during eight heating seasons in houses of Tomsk (Russia).

All table rows are the following.

Date - date in Windows format.
M1 - the mass of the input water (heat carrier) per day.
M2 - the mass of the output water. If the residential building has an open heating system (hot water flows from the heating system), M2 is less than M1.
Delta_M = difference M2-M1. It is the technological parameter that allows the equipment observation for buildings with the closed system.
T1 - the average temperature of the heating carrier in the input of the heating system. It is the independent variable from home characteristics.
T2 - the average temperature of the heating carrier in the output. It is the dependent variable both from T1 and heating consumption at the building.
Delta_T = difference T2-T1.
Q =M1*(T2-T1) - amount of the consumed heating in Gcal.
USPD - ID of the heating meter. Some residential buildings have not the only one heating meters.
YYYYMM - date in the format year-month YYYYMM.
Registrated - heating or heating plus hot water that under registration.
Scheme - the type of the heating system (opened or closed).
Type - code system-load (4 digits). First digit 1 is opened system, 2 is a closed system. The second digit 0 is heating, 1 is heating and hot water supply. The third and fourth digits are floor amount (01, 02, 03, ..., 17).
Area - the area of building that heating meter is served.
Floors - the amount of building floors.
Walls_material - walls material.
Year_of_construction - the year of building construction.
Area_of_building - total area of the building.
Temperature - outdoor temperature by RosHydromet website.
Inhabitants - the amount of inhabitants in the house.

The Python program "viborka_house.zip" allows you to select from the file "teplo.csv" rows that belongs to the same heating meter USPD. This allows receiving of heat consumption series from a particular house and the outside air temperature in this day. After "viborka.py" starting the user enters the USPD number, names of the input, and output files.

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This dataset contains (1) the Simulink model of a three-phase photovoltaic power system with passive anti-islanding protections like over/under current (OUC), over/under voltage (OUV), over/under frequency (OUF), rate of change of frequency (ROCOF), and dc-link voltage and (2) the results in the voltage source converter and the point of common coupling of the photovoltaic system during islanding operation mode and detection times of analyzed anti-islanding methods.

Instructions: 

The anti-islanding protection relays are included in the "Relay Protection Bus B20 (20 kV)" subsystem.

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This dataset is in support of my research paper 'Comparison of ElectroMagnetic Emissions & Harmonic Analysis of 20 HP Motor Controlled by 3L NPC Inverter '.

Preprint : https://doi.org/10.36227/techrxiv.19687041.v1

This is useful for manufacturers and r&d engineers for product costing. For more information, results, conclusions on this, pls read research paper.

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Following the success of the previous editions at WCCI 2018 (Rio de Janeiro, Brazil) and CEC/GECCO 2019 (New Zealand and Prague, Czechia) we are launching a more challenging algorithm competition at major international conferences in the field of computational intelligence. This WCCI & GECCO 2020 competition proposes two testbeds in the energy domain:

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Following the success of the previous editions (CEC, GECCO, WCCI), we are launching a more challenging competition at major conferences in the field of computational intelligence. This GECCO 2021 competition proposes two tracks in the energy domain:

Last Updated On: 
Wed, 02/24/2021 - 10:38
Citation Author(s): 
Fernando Lezama, Joao Soares, Bruno Canizes, Zita Vale, Ruben Romero

This dataset contains the hourly generation and consumption profiles used to  estimate the flexibility at TSO-DSO interface.

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The dataset is generated by performing different MiTM attacks in the synthetic electric grid in RESLab testbed at Texas A&M University, US. The testbed primarily consists of a dynamic power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master and Elasticsearch's Packetbeat index. There are raw and processed files that can be used by security enthusiasts to develop new features and also to train IDS using our feature space respectively.

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