Demand Profile Dates
This resource contains the historic and forecast profile dates used to convert our Cardinal Point forecasts into half hourly demand forecasts between the start of 2018 up to 14 days ahead.
The profile dates are NESO's estimate at the historic date which will most closely match the forecast date in terms of weather, embedded generation, time of year, recurring calendar activities and special events. These dates can change as we get closer to real time as our weather forecasts are updated and certainty improves.
CKAN Data API
Access resource data via a web API with powerful query support.
Further information in the main CKAN Data API and DataStore documentation.
The Data API can be accessed via the following actions of the CKAN action API.
Query example (first 5 results) |
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https://api.neso.energy/api/3/action/datastore_search?resource_id=48862d1a-f285-4709-a48c-1c0e8e7b37c3&limit=5
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Query example (via SQL statement) |
https://api.neso.energy/api/3/action/datastore_search_sql?sql=SELECT * from "48862d1a-f285-4709-a48c-1c0e8e7b37c3" LIMIT 5
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A simple ajax (JSONP) request to the data API using jQuery.
var data = { resource_id: '48862d1a-f285-4709-a48c-1c0e8e7b37c3', // the resource id limit: 5 // get 5 results }; $.ajax({ url: 'https://api.neso.energy/api/3/action/datastore_search', data: data, dataType: 'jsonp', success: function(data) { alert('Total results found: ' + data.result.total) } });
A simple ajax (JSONP) request to the data API using jQuery.
import urllib2 url = 'https://api.neso.energy/api/3/action/datastore_search?resource_id=48862d1a-f285-4709-a48c-1c0e8e7b37c3&limit=5' fileobj = urllib2.urlopen(url) print fileobj.read()
Data Explorer
Data Explorer
Table Information
FORECAST_DATE
Title | Forecast Date |
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Type | date |
Description | The date for which the profile date was used. |
Comment | |
Example | 2018-01-01 |
Unit |
PROFILE_DATE
Title | Profile Date |
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Type | date |
Description | The profile date that has been chosen for that forecast date. |
Comment | The profile dates are NESO's estimate at the historic date which will most closely match the forecast date in terms of weather, embedded generation, time of year, recurring calendar activities and special events. These dates can change as we get closer to real time as our weather forecasts are updated and certainty improves. |
Example | 2017-01-01 |
Unit |