Historic Day Ahead Demand Forecasts
All historic day ahead demand forecasts from 2018 to the most recent forecast.
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=9847e7bb-986e-49be-8138-717b25933fbb&limit=5
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Query example (via SQL statement) |
https://api.neso.energy/api/3/action/datastore_search_sql?sql=SELECT * from "9847e7bb-986e-49be-8138-717b25933fbb" LIMIT 5
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A simple ajax (JSONP) request to the data API using jQuery.
var data = { resource_id: '9847e7bb-986e-49be-8138-717b25933fbb', // 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=9847e7bb-986e-49be-8138-717b25933fbb&limit=5' fileobj = urllib2.urlopen(url) print fileobj.read()
Data Explorer
Data Explorer
Table Information
DAYSAHEAD
Title | Days Ahead |
---|---|
Type | integer |
Description | Number of days in advance the forecast was produced |
Comment | |
Example | 1 |
Unit | Days |
TARGETDATE
Title | Target Date |
---|---|
Type | date |
Description | The date the demand forecast is for |
Comment | |
Example | 2021-04-24T00:00:00Z |
Unit |
FORECASTDEMAND
Title | Forecast Demand |
---|---|
Type | integer |
Description | National day ahead demand forecast values. |
Comment | |
Example | 25269 |
Unit | MW |
CARDINALPOINT
Title | Cardinal Point |
---|---|
Type | string |
Description | Electricity demand fluctuates during a day depending on how much energy people, businesses and industries are using at that moment in time. As this electricity demand goes up and down we get characteristic peaks and troughs, with some of these peaks and troughs appearing every single day at similar times. These we call cardinal points and are the points during the day that we forecast demand for. |
Comment | |
Example | 2A |
Unit |
CP_TYPE
Title | Cardinal Point Type |
---|---|
Type | string |
Description | Fixed, Trough or Peak. Cardinal points (CPs) can either be fixed (occur at a fixed time), trough (minimum demands during a set period of the day) or peak (maximum demands during a set period of the day). These are represented through the first letter of the point type (F, T or P) |
Comment | |
Example | P |
Unit |
CP_ST_TIME
Title | Cardinal Point Start Time |
---|---|
Type | number |
Description | The time when a particular cardinal point (CP) starts during the day. This is given relative to the timezone in effect in the UK at the forecast time and date. |
Comment | |
Example | 930 |
Unit |
CP_END_TIME
Title | Cardinal Point End Time |
---|---|
Type | number |
Description | The time when a particular cardinal point (CP) ends during the day. This is given relative to the timezone in effect in the UK at the forecast time and date. |
Comment | |
Example | 1030 |
Unit |
F_Point
Title | Forecasting Point |
---|---|
Type | string |
Description | Publishing forecasts of forecasting points: overnight demand minimum, daytime demand maximum, daytime demand minimum and evening demand maximum. |
Comment | Forecasting point 1: Overnight minimum (OM): minimum national demand between half hour ending 00:30 and 07:30. Forecasting point 2: Daytime peak (DM): maximum national demand between half hour ending 08:00 and 13:00. Forecasting point 3: Daytime minimum (Dm): minimum national demand between half hour ending 13:30 and 16:30. Forecasting point 4: Evening peak (EM): maximum national demand between half hour ending 17:00 and 24:00. |
Example | Om |
Unit |
FORECAST_TIMESTAMP
Title | Forecast Timestamp |
---|---|
Type | datetime |
Description | The date and time at which the forecast was made. |
Comment | Rarely, some forecasts had to be recovered from emails due to some overwriting of data. This means that some of our timestamps occur after the forecast was made as this is the time at which the forecasts were recovered. |
Example | 2018-03-28T14:57:45Z |
Unit |