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Electricity

machine-learning

Files Size Format Created Updated License Source
3 6MB arff csv zip 4 years ago 4 years ago Open Data Commons Public Domain Dedication and License
The resources for this dataset can be found at https://www.openml.org/d/151 Author: M. Harries, J. Gama, A. Bifet Source: Joao Gama - 2009 Please cite: None Electricity is a widely used dataset described by M. Harries and analyzed by J. Gama (see papers below). This data was collected from the read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
electricity_arff 3MB arff (3MB)
electricity 3MB csv (3MB) , json (8MB)
electricity_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 3MB zip (3MB)

electricity_arff  

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This is a preview version. There might be more data in the original version.

electricity  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
date 1 number (default)
day 2 number (default)
period 3 number (default)
nswprice 4 number (default)
nswdemand 5 number (default)
vicprice 6 number (default)
vicdemand 7 number (default)
transfer 8 number (default)
class 9 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/machine-learning/electricity
data info machine-learning/electricity
tree machine-learning/electricity
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/electricity/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/electricity/r/0.arff

curl -L https://datahub.io/machine-learning/electricity/r/1.csv

curl -L https://datahub.io/machine-learning/electricity/r/2.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/machine-learning/electricity/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/machine-learning/electricity/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/machine-learning/electricity/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/machine-learning/electricity/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

The resources for this dataset can be found at https://www.openml.org/d/151

Author: M. Harries, J. Gama, A. Bifet
Source: Joao Gama - 2009
Please cite: None

Electricity is a widely used dataset described by M. Harries and analyzed by J. Gama (see papers below). This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.

The dataset (originally named ELEC2) contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends).

The data was normalized by A. Bifet.

Attribute information

  • Date: date between 7 May 1996 to 5 December 1998. Here normalized between 0 and 1
  • Day: day of the week (1-7)
  • Period: time of the measurement (1-48) in half hour intervals over 24 hours. Here normalized between 0 and 1
  • NSWprice: New South Wales electricity price, normalized between 0 and 1
  • NSWdemand: New South Wales electricity demand, normalized between 0 and 1
  • VICprice: Victoria electricity price, normalized between 0 and 1
  • VICdemand: Victoria electricity demand, normalized between 0 and 1
  • transfer: scheduled electricity transfer between both states, normalized between 0 and 1

Relevant papers

M. Harries. Splice-2 comparative evaluation: Electricity pricing. Technical report, The University of South Wales, 1999.
J. Gama, P. Medas, G. Castillo, and P. Rodrigues. Learning with drift detection. In SBIA Brazilian Symposium on Artificial Intelligence, pages 286–295, 2004.

Datapackage.json

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