This data contains a three-phase time-series load flow for 100 days with a 10-min interval. The distribution is located in the U.K.

This data file contains

1. Three-phase load data for Matlab: "loaddata.mat". The variable is named "three_phase" in Matlab. Use** load("loaddata.mat")** to load data in Matlab.

2. Three-phase load data for Mathematica: "WPD_load.wl". The variable is named "threephasedata" in Mathematica. Use the function **ReadList** to load data in Mathematica.

Note, these two files contain the same data.

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The standard for measurement of solar irradiance utilizes the units of watts per meter squared (W/m2). Irradiance meters are both costly and limited in the ability to measure low irradiance values. With a lower cost and higher sensitivity in low light conditions, light meters measure luminous flux per unit area (illuminance) utilizing the units of lumens per meter squared or lux (lx).

Spreadsheet with ASTMG data with conversion of solar irradiance to light illuminance

Spreadsheet with results of indoor lab testing

Spreadsheet with results of outdoor testing

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Power transmission system losses can typically represent from five to ten percent of the total generation, a quantity worth millions of dollars per year. The purpose of loss allocation in the context of pool dispatch is to assign to each individual generation and load the responsibility of paying for part of the system transmission losses. Since the system losses are non-separable, non-linear functions of the real power generation and loads, the allocation of transmission loss is a challenging and contentious issue in a fully deregulated system.

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Dataset of the paper entitled: "Voltage Disturbance Classification for Transmission Grid Using Wavelets and Deep Learning"

In the dataset, there is the electrical transmission system modeled in Simulink. It also contains the codes to generate the data from the model, extract images from data processing (in this case, a continuous wavelet transform), and image processing. Finally, the program to train the network is also provided. All codes are in M-FILE format.

1º ) Open the .slx file with modeled system;

2º) data_sinal_generation.m contains the code for generate signal through Simulink Model;

3º) image_data.m performs the extraction of 2-D images (training data);

4º) test_data.m and imteste.m performs generation of data and extraction of 2-D images (test data);

5º) cnn_pattern.m performs the CNN training

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An emission rate-based carbon tax is applied to fossil-fueled generators along with a Smart Grid resource allocation (SGRA) approach. The former reduces the capacity factors (CFs) of base load serving fossil-fueled units, while the latter reduces the CFs of peak load serving units. The objective is to quantify the integration of the carbon tax and the SGRA approach on CO2 emissions and electricity prices in a multi-area power grid.

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Data for 24-node, 54-node, 86-node, and 138-node test networks for reliability-oriented distribution expansion planning applications; and the results for 24-node and 54-node systems.

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This data is related to the paper: "Energy hardware and workload aware job scheduling towards interconnected HPC environments". In includes two energy models for nodes equipped with Intel Gold and Platinum CPUs, and eight application's data, to be used to estimate runtime, energy, and power at different frequencies. For detailed information on the energy model and how to use it, please read the paper.

First model: 2x Platinum 8168 CPU [2.70GHz-1.20GHz]24C, TDP 205W, 12 x 16GB DDR4 SDRAM

Second model: 2x Gold 6254 CPU [3.10GHz-1.20GHz] 18C,TDP 200W, 12 x 32GB DDR4 SDRAM

The data is meant and formatted to be used together with EAMC-policy presented in: "Energy hardware and workload aware job scheduling towards interconnected HPC environments". Its code implemented in Slurm Simulator is available at: https://github.com/marcodamico/slurm_simulator_eamc-policy.git

Use the paper information, the repo readme, and the paper appendix to get started, for any question contact the authors.

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The large variability of system and types of heating load is a feature of the commercial metering of thermal energy. Heating consumption depends on many factors, for example, wall and roof material, floors number, system (opened and closed) etc. The daily data from heating meters in the residential buildings are presented in this dataset for comparing the thermal characteristics. These data are supplemented by floors number, wall material and year of construction, as well as data on average daily outdoor temperatures.

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*This datasheet is being updated progressively to provide more details.

**This datasheet provides the phasor measurement data in actual power systems.**

These PMU data were recorded during a Low Frequency Oscillation incident and a Short Circuit Incident, respectively.

These PMU data are used for the studies in wide-area control systems (WACS) and PMU data compressions.

**Please cite this datasheet and the papers in your work if they help.**

## Refer to the documentation file for detailed information.

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