Abstract

This repository includes the energy consumption data set of a Data Server that is running in the facilities of the Information Technology Center (CTI) of the Escuela Superior Politécnica del Litoral (ESPOL). In addition, it includes Matlab scripts to perform the prediction of energy consumption. The data acquisition equipment was implemented in the Electronic Prototype Development Matter of the Faculty of Electrical Engineering and Computing (FIEC), based on the ESP32 hardware.

Link of the Electronic Design of the energy consumption meter: https://vasanza.blogspot.com/2021/02/ensemble-de-pcbs-de-desarrollo-de.html

⭐ When using this resource, please cite the original publication:

Estrada, R., Asanza, V., Torres, D., Bazurto, A., & Valeriano, I. (2022). Learning-based Energy Consumption Prediction. Procedia Computer Science, 203, 272-279, doi: https://doi.org/10.1016/j.procs.2022.07.035.

Instructions:

Data were collected from an HP Z440 workstation for 245 days (35 weeks) with a sampling rate of one value per second. The columns represent the following variables:

Voltage (V)
Currennt (A)
Power (PA) - Watts (W)
Frecuency - Hertz (Hz)
Active Energy - kilowatts per hour (KWh)
Power factor - Adimentional
ESP32 temperature - Centigrade Degrees (°C)
CPU consumption - Percentage (%)
CPU power consumption - Percentage (%)
CPU temperature - Centigrade Degrees (°C)
GPU consumption - Percentage (%)
GPU power consumption - Percentage (%)
GPU temperature - Centigrade Degrees (°C)
RAM memory consumption - Percentage (%)
RAM memory power consumption - Percentage (%)

Source code:

https://github.com/vasanza/EnergyConsumptionPrediction

Dataset Files

main.zip (3.66 MB)

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Data Server Energy Consumption Dataset

Abstract

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