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API Wrappers for the Energy Markets

Project description

Energy Trading API Wrappers

This package aims to build Pandas-based API wrappers for Energy Markets Data coming from publicly available sources. The results are returned in the Pandas dataFrame format unless specified otherwise.

disclaimer While readily useable, this API library is under constant in development. The reliability of the data from the API list depends on the sources. Enjoy!

Supported APIs:

Australia

Singapore

Japan

Global

Installation

  • Python 3.6 or 3.7

To install, use pip :

$ pip install energy-trading-api

Requirements

  • Python 3.7

Usage

AUSTRALIA

Western Australian Wholesale Electricity Market (WEM)
from energy_trading_api import australiaWEM 
df = australiaWEM.loadForecast()
df1 = australiaWEM.demandSideProgrammePrices(year="2019")

WEM Data API Documentation

ACCC LNG Netback Price Series
from energy_trading_api import australiaLNG
netback,netforward,merged = australiaLNG.acccNetbackPrice()
print(netback.head())

ACCC LNG Netback Price Series Documentation

Australian REC Register
from energy_trading_api import australiaREC 
df = australiaREC.recDay("2019-01-01")

REC Registry API Documentation

AEMC Gas Scheme Register
from energy_trading_api import australiaNG 
df = australiaNG.pipelineRegister()
df1 = australiaNG.pipelineRegisterSearch(state="NSW",operator="APA Group")

AEMC Gas Scheme Register Website

Western Australia Gas Bulletin Board
from energy_trading_api import wagbb 
wagbb.capacityOutlook()

WAGBB API Documentation

Australian Bureau of Meteorology

Retrieves BOM records into a pandas dataframe, given Product and location.

For instance, Bankstown time series data has a product code of IDN60901.94765.

To return Air Temp, Apparent Temp Rel Humidity, Cloud data in a pandas df, simply do:

from energy_trading_api import australiaBOM 
df1 = australiaBOM.airTemp('IDN60901.94765')
df2 = australiaBOM.apparentTemp('IDN60901.94765')
df2 = australiaBOM.relativeHumidity('IDN60901.94765')
df3 = australiaBOM.cloud('IDN60901.94765')

You can also access the entire dataset by doing:

from energy_trading_api import australiaBOM 
df, df_header = australiaBOM.__call_api_BOM('IDN60901.94765')
# e.g. dew point:
print(df['dewpt'].to_string())

Bankstown BOM Data Example

SINGAPORE

Singapore National Electricity Market (NEMS)
from energy_trading_api import singaporeNEMS 
df = singaporeNEMS.singaporeUSEP(date="2019-01-01")

JAPAN

Japan Electric Power Exchange
from energy_trading_api import jepx 
df = jepx.spotLatest()
df = jepx.spotLatest("20190101")
Kyushu Electric Power Company (Kyuden)
from energy_trading_api import japanElectricity as je 
df = je.kyushuElectricdemandJapanese()
df1 = je.kyushuElectricdemandJapanese(day="20190101")
Chubu Electric Power Company (Chuden)
from energy_trading_api import japanElectricity as je 
df = je.chubuElectricdemandJapanese()
df1 = je.chubuElectricDemandJapaneseRange(begtime="20190101",endtime="20190101")
Tokyo Electric Power Company (TEPCO)
from energy_trading_api import japanElectricity as je 
df = je.tepcoElectricDemandHistoricalJapanese("2018")
df1 = je.tepcoElectricDemandCurrentJapanese()

GLOBAL

U.S. Energy Information Administration
from energy_trading_api import eia_api 
df = eia_api.ngAustraliaProduction("<YOUR-API-KEY-HERE>")

EIA-Python Documentation EIA API Documentation

Project details


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