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  • Mesure data
GreEn-ER - Dataset of electricity consumption
  • Contributor Gustavo Felipe Martin Nascimento
Dataset of electricity consumption of the GreEn-ER Building Located in Grenoble
Read me file
readme.txt
Read me file
Dataset - GreEn-ER - Dataset of electricity consumption


================================================================================================================================================================
********
Authors:
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Gustavo Felipe MARTIN NASCIMENTO, Frederic WURTZ, Benoit DELINCHANT, Patrick KUO-PENG, Nelson JHOE BATISTELA

================================================================================================================================================================
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Contact:
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gustavo-felipe.martin-nascimento@g2elab.grenoble-inp.fr ; gustavofmn@hotmail.com

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Data description:
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This dataset provides electricity consumption data collected from the building management system of GreEn-ER. This building, located in Grenoble, hosts
Grenoble-INP Ense³ Engineering School and the G2ELab (Grenoble Electrical Engineering Laboratory).
It brings together in one place the teaching and research actors around new energy technologies.

The electricity consumption of the building is highly monitored with plus than 300 meters.

The data from each meter is available in one csv file, which contains two columns.

One contains the Timestamp and the other contains de electricity consumption in kWh.

The sampling rate for all data is 10 min.

There are data available for 2017 and 2018.

The dataset also contains data of the external temperature for 2017 and 2018.

The files are structured as follows:

- The main folder called "Data" contains 2 sub-folders, each one corresponding to one year (2017 and 2018).

- Each sub-folder contains 3 other sub-folders, each one corresponding to a sector of the building.

- The main folder "Data" also contains the csv files with the electricity consumption data of the whole building and a file called "Temp.csv" with the temperature data.

- The separator used in the csv files is ";".

- The sampling rate is 10 min and the unity of the consumption is kWh. It means that each sample corresponds to the energy consumption in these 10 minutes.
So if the user wants to retrieve the mean power in this period (that corresponds to each sample), the value must be multiplied by 6.

- Four Jupyter Notebook files, a format that allows combining text, graphics and code in python are also available.
These files allow exploring all the data within the dataset.

- These jupyter notebook files contains all the metadata necessary for understanding the system, like drawings of the system design, of the building etc.

- Each file is named by the number of its meter. These numbers can be retrieved in tables and drawings available in the Jupyter Notebooks.

- A couple of csv files with the system design are also available. They are called "TGBT1_n.csv", "TGBT2_n.csv" and "PREDIS-MHI_n.csv".

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2020 06 11
The size of this dataset is beetween 10 and 100 Mb
Archive files
dataetpercido.zip
2020 06 11
62.42 MB
  • 2017 /
  • 2017 / 585.csv 1 483 813 ko
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  • 2017 / PREDIS_MHI /
  • 2017 / PREDIS_MHI / 637.csv 1 286 660 ko
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  • 2017 / Temp.csv 235 654 ko
  • 2017 / TGBT1 /
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  • 2018 / PREDIS_MHI / 689.csv 1 286 494 ko
  • 2018 / PREDIS_MHI / 690.csv 1 286 521 ko
  • 2018 / PREDIS_MHI / 691.csv 1 235 065 ko
  • 2018 / PREDIS_MHI / 692.csv 1 254 583 ko
  • 2018 / PREDIS_MHI / 693.csv 1 286 523 ko
  • 2018 / PREDIS_MHI / 694.csv 1 337 952 ko
  • 2018 / PREDIS_MHI / 695.csv 1 286 521 ko
  • 2018 / PREDIS_MHI / 696.csv 1 286 521 ko
  • 2018 / PREDIS_MHI / 697.csv 1 183 604 ko
  • 2018 / PREDIS_MHI / 698.csv 1 286 494 ko
  • 2018 / PREDIS_MHI / 699.csv 1 286 521 ko
  • 2018 / PREDIS_MHI / 700.csv 1 286 521 ko
  • 2018 / PREDIS_MHI / 701.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 702.csv 1 337 952 ko
  • 2018 / PREDIS_MHI / 703.csv 1 314 153 ko
  • 2018 / PREDIS_MHI / 704.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 705.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 706.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 707.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 708.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 709.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 710.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 711.csv 1 286 520 ko
  • 2018 / PREDIS_MHI / 712.csv 1 337 967 ko
  • 2018 / PREDIS_MHI / 713.csv 1 389 406 ko
  • 2018 / PREDIS_MHI / 714.csv 1 235 058 ko
  • 2018 / PREDIS_MHI / 715.csv 1 183 600 ko
  • 2018 / PREDIS_MHI / 716.csv 1 286 516 ko
  • 2018 / PREDIS_MHI / 717.csv 1 286 516 ko
  • 2018 / PREDIS_MHI / 718.csv 1 183 600 ko
  • 2018 / PREDIS_MHI / 719.csv 1 235 058 ko
  • 2018 / PREDIS_MHI / 720.csv 1 289 063 ko
  • 2018 / PREDIS_MHI / 721.csv 1 286 516 ko
  • 2018 / PREDIS_MHI / 722.csv 1 286 516 ko
  • 2018 / PREDIS_MHI / 723.csv 1 235 051 ko
  • 2018 / PREDIS_MHI / 724.csv 1 183 582 ko
  • 2018 / PREDIS_MHI / 725.csv 1 389 424 ko
  • 2018 / PREDIS_MHI / 726.csv 1 286 505 ko
  • 2018 / PREDIS_MHI / 827.csv 1 337 947 ko
  • 2018 / PREDIS_MHI / 828.csv 1 337 963 ko
  • 2018 / Temp.csv 191 722 ko
  • 2018 / TGBT1 /
  • 2018 / TGBT1 / 500.csv 1 389 407 ko
  • 2018 / TGBT1 / 501.csv 1 337 947 ko
  • 2018 / TGBT1 / 502.csv 1 337 948 ko
  • 2018 / TGBT1 / 503.csv 1 183 575 ko
  • 2018 / TGBT1 / 504.csv 1 337 949 ko
  • 2018 / TGBT1 / 505.csv 1 337 947 ko
  • 2018 / TGBT1 / 506.csv 1 338 589 ko
  • 2018 / TGBT1 / 507.csv 1 332 258 ko
  • 2018 / TGBT1 / 508.csv 1 389 434 ko
  • 2018 / TGBT1 / 509.csv 1 389 432 ko
  • 2018 / TGBT1 / 510.csv 1 389 433 ko
  • 2018 / TGBT1 / 511.csv 1 183 540 ko
  • 2018 / TGBT1 / 512.csv 1 337 974 ko
  • 2018 / TGBT1 / 513.csv 1 337 975 ko
  • 2018 / TGBT1 / 514.csv 1 389 432 ko
  • 2018 / TGBT1 / 515.csv 1 389 433 ko
  • 2018 / TGBT1 / 516.csv 1 337 974 ko
  • 2018 / TGBT1 / 517.csv 1 389 434 ko
  • 2018 / TGBT1 / 518.csv 1 389 432 ko
  • 2018 / TGBT1 / 519.csv 1 389 433 ko
  • 2018 / TGBT1 / 520.csv 1 337 975 ko
  • 2018 / TGBT1 / 521.csv 1 389 432 ko
  • 2018 / TGBT1 / 522.csv 1 389 433 ko
  • 2018 / TGBT1 / 523.csv 1 337 974 ko
  • 2018 / TGBT1 / 524.csv 1 337 949 ko
  • 2018 / TGBT1 / 525.csv 1 337 947 ko
  • 2018 / TGBT1 / 526.csv 1 337 948 ko
  • 2018 / TGBT1 / 527.csv 1 286 501 ko
  • 2018 / TGBT1 / 528.csv 1 235 047 ko
  • 2018 / TGBT1 / 529.csv 1 337 948 ko
  • 2018 / TGBT1 / 530.csv 1 388 723 ko
  • 2018 / TGBT1 / 531.csv 1 337 947 ko
  • 2018 / TGBT1 / 532.csv 1 286 490 ko
  • 2018 / TGBT1 / 533.csv 1 389 434 ko
  • 2018 / TGBT1 / 534.csv 1 389 432 ko
  • 2018 / TGBT1 / 535.csv 1 337 974 ko
  • 2018 / TGBT1 / 536.csv 1 337 974 ko
  • 2018 / TGBT1 / 537.csv 1 337 975 ko
  • 2018 / TGBT1 / 538.csv 1 389 432 ko
  • 2018 / TGBT1 / 539.csv 1 337 974 ko
  • 2018 / TGBT1 / 540.csv 1 337 974 ko
  • 2018 / TGBT1 / 541.csv 1 389 407 ko
  • 2018 / TGBT1 / 542.csv 1 389 405 ko
  • 2018 / TGBT1 / 543.csv 1 337 948 ko
  • 2018 / TGBT1 / 544.csv 1 386 070 ko
  • 2018 / TGBT1 / 545.csv 1 389 405 ko
  • 2018 / TGBT1 / 546.csv 1 337 948 ko
  • 2018 / TGBT1 / 547.csv 1 338 000 ko
  • 2018 / TGBT1 / 548.csv 1 245 530 ko
  • 2018 / TGBT1 / 549.csv 1 183 619 ko
  • 2018 / TGBT1 / 550.csv 1 337 999 ko
  • 2018 / TGBT1 / 551.csv 1 372 366 ko
  • 2018 / TGBT1 / 552.csv 1 383 317 ko
  • 2018 / TGBT1 / 553.csv 1 337 999 ko
  • 2018 / TGBT1 / 554.csv 1 337 999 ko
  • 2018 / TGBT1 / 555.csv 1 337 975 ko
  • 2018 / TGBT1 / 556.csv 1 337 973 ko
  • 2018 / TGBT1 / 557.csv 1 183 597 ko
  • 2018 / TGBT1 / 558.csv 1 286 535 ko
  • 2018 / TGBT1 / 559.csv 1 183 597 ko
  • 2018 / TGBT1 / 560.csv 1 337 955 ko
  • 2018 / TGBT1 / 561.csv 1 286 497 ko
  • 2018 / TGBT1 / 562.csv 1 297 463 ko
  • 2018 / TGBT1 / 563.csv 1 347 363 ko
  • 2018 / TGBT1 / 564.csv 1 347 363 ko
  • 2018 / TGBT1 / 565.csv 1 310 338 ko
  • 2018 / TGBT1 / 566.csv 1 297 463 ko
  • 2018 / TGBT1 / 567.csv 1 397 264 ko
  • 2018 / TGBT1 / 568.csv 1 297 463 ko
Other metadata
  • External Identifiers:

  • Subjects:

    Computer Science, Engineering
  • Keywords:

    Energy consumption, Tertiary Building, Electricity consumption
  • Corresponding tasks:

    regression analysis, anomaly detection, pattern extraction, prediction, clustering, Activity recognition- tracking
  • Encoding data format:

    csv

Gustavo Felipe Martin Nascimento (2020) GreEn-ER - Dataset of electricity consumption. [Data set]. Published 2020 via Perscido-Grenoble-Alpes;

Gustavo Felipe Martin Nascimento (2020) GreEn-ER - Dataset of electricity consumption. [Data set]. Published 2020 via Perscido-Grenoble-Alpes