Steel Industry Energy Consumption
Donated on 8/13/2023
The data is collected from a smart small-scale steel industry in South Korea.
Dataset Characteristics
Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Regression
Feature Type
Real, Categorical
# Instances
35040
# Features
9
Dataset Information
Additional Information
The information gathered is from the DAEWOO Steel Co. Ltd in Gwangyang, South Korea. It produces several types of coils, steel plates, and iron plates. The information on electricity consumption is held in a cloud-based system. The information on energy consumption of the industry is stored on the website of the Korea Electric Power Corporation (pccs.kepco.go.kr), and the perspectives on daily, monthly, and annual data are calculated and shown.
Has Missing Values?
No
Introductory Paper
By Sathishkumar V E, Changsun Shin, Yongyun Cho. 2021
Published in Building Research & Information, Vol. 49. no. 1, pp. 127-143
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| date | Other | Date | no | ||
| Usage_kWh | Feature | Continuous | Industry Energy Consumption | kWh | no |
| Lagging_Current_Reactive.Power_kVarh | Feature | Continuous | kVarh | no | |
| Leading_Current_Reactive_Power_kVarh | Feature | Continuous | kVarh | no | |
| CO2(tCO2) | Feature | Continuous | ppm | no | |
| Lagging_Current_Power_Factor | Feature | Continuous | % | no | |
| Leading_Current_Power_Factor | Feature | Continuous | % | no | |
| NSM | Feature | Integer | s | no | |
| WeekStatus | Feature | Categorical | Weekend (0) or a Weekday(1) | no | |
| Day_of_week | Feature | Categorical | Sunday, Monday, ..., Saturday | no |
0 to 10 of 11
Additional Variable Information
Data Variables Type Measurement Industry Energy Consumption Continuous kWh Lagging Current reactive power Continuous kVarh Leading Current reactive power Continuous kVarh tCO2(CO2) Continuous ppm Lagging Current power factor Continuous % Leading Current Power factor Continuous % Number of Seconds from midnight Continuous S Week status Categorical (Weekend (0) or a Weekday(1)) Day of week Categorical Sunday, Monday …. Saturday Load Type Categorical Light Load, Medium Load, Maximum Load
Class Labels
Light Load, Medium Load, Maximum Load
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset steel_industry_energy_consumption = fetch_ucirepo(id=851) # data (as pandas dataframes) X = steel_industry_energy_consumption.data.features y = steel_industry_energy_consumption.data.targets # metadata print(steel_industry_energy_consumption.metadata) # variable information print(steel_industry_energy_consumption.variables)
V E,Sathishkumar, Shin,Changsun, and Cho,Yongyun. (2023). Steel Industry Energy Consumption. UCI Machine Learning Repository. https://doi.org/10.24432/C52G8C.
@misc{misc_steel_industry_energy_consumption_851,
author = {V E,Sathishkumar, Shin,Changsun, and Cho,Yongyun},
title = {{Steel Industry Energy Consumption}},
year = {2023},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C52G8C}
}Keywords
Creators
Sathishkumar V E
srisathishkumarve@gmail.com
Sunchon National University
Changsun Shin
Yongyun Cho
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.