Electrical Grid Half Hourly (UK) by Kaggle | Demographics and Population Studies

About this Dataset

Electrical Grid Half Hourly (UK)

Investigating Supply and Demand of Electricity in the UK

About this dataset

This dataset contains a wealth of information on the electrical half-hourly data for Great Britain from 2008 up until present day. This dataset is sourced from both the Elexon Portal and National Grid, providing you with an in-depth view into electricity supply and demand in the UK. It includes conventional generation, wind generation, nuclear generation, pumped storage and imports & exports. With columns such as ELEXM_SETTLEMENT_DATE, ELEXM_SETTLEMENT_PERIOD, ELEXM_UTC etc., this dataset is ideal for anyone looking to gain a truly comprehensive understanding of current energy situation in Britain!

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How to use the dataset

Introduction
This data set contains compiled and cleaned half-hourly electricity data for Great Britain. It is sourced from various providers such as Elexon Portal and National Grid, making it a great tool for studying electrical supply and demand in the UK.
This guide will provide an overview of this dataset, walking through the required steps on how to use this dataset effectively.

Getting Familiar with the Data:
The first step in using this dataset is to get familiar with its features. This includes looking at the columns/variables available, their descriptions/units, and meaning. Under each column you'll find additional information about its data type (eg; integer or float) which is helpful to understand while performing any kind of analysis with it. Another excellent way to explore this data set would be by simply looking at some examples of each column's contents by printing out a few rows of the table so one can further investigate them based of those values listed thereupon. Doing so should give you more clarity over what type of questions you can answer with your analyses – keeping in mind that not all datasets are suitable for addressing all potential queries concerning research inquiry!

Understanding Relationships: After getting familiarised with its features & attributes, it's important to start understanding how they're related to each other which makes up our overall analysis process when dealing with any given dataset(s). To do that we take into consideration variable characteristics - such as presence or absence (or correlations) between certain columns - when ultimately constructing relationships among diverse elements comprising specific cases under study through various operations like merging & merging adjacent tables within the same frame wor for transforming raw input information into meaningful knowledge derived after completing analytics task(s). This all helps us gain insight on patterns present throughout the entire collection as well as individual items themselves whether individually or collectively over time leading towards desired outputs necessary to answer particular questions being asked about underlying trends found inside datasets used at hand!

Performing Analyses: We then start running analytical approaches directly on given raw information extracted during the previous step (understanding relationships) either via compilation-based processing methods within statistical environments like R studio/Python Pandas libraries etcetera; these tools allow us to create models upon activating suitable algorithms processing tools helping visualize pattern displays interpreting different feature combinations under scrutiny when focusing particularly towards better understanding interdependencies/correlations existing among different case variables studied until reaching desired insights required solving existing problems coming up next when proceeding towards generating user-specific targeted solutions depending context per question posed during initial exploratory use cases outlined

Research Ideas

Analyzing long-term trends in electricity generation and consumption of different sources over time.

Using machine learning algorithms to predict future energy consumption, production and pricing in the UK electricity market.

Developing more efficient methods of powering homes, businesses and other organizations based on energy consumption patterns from this dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: espeni.csv

Column name	Description
ELEXM_SETTLEMENT_DATE	Date of the settlement period. (Date)
ELEXM_SETTLEMENT_PERIOD	Half-hourly period of the settlement. (Integer)
ELEXM_utc	Time in UTC format. (Time)
ELEXM_localtime	Time in local time format. (Time)
ELEXM_ROWFLAG	Flag indicating the validity of the data. (Integer)
NGEM_ROWFLAG	Flag indicating the validity of the data. (Integer)
POWER_ESPENI_MW	Total electricity demand in the UK. (Float)
POWER_ELEXM_CCGT_MW	Combined cycle gas turbine electricity generation. (Float)
POWER_ELEXM_OIL_MW	Oil electricity generation. (Float)
POWER_ELEXM_COAL_MW	Coal electricity generation. (Float)
POWER_ELEXM_NUCLEAR_MW	Nuclear electricity generation. (Float)
POWER_ELEXM_WIND_MW	Wind electricity generation. (Float)
POWER_ELEXM_PS_MW	Pumped storage electricity generation. (Float)
POWER_ELEXM_NPSHYD_MW	Non-pumped storage hydro electricity generation. (Float)
POWER_ELEXM_OCGT_MW	Open cycle gas turbine electricity generation. (Float)
POWER_ELEXM_OTHER_POSTCALC_MW	Other electricity generation. (Float)
POWER_ELEXM_BIOMASS_POSTCALC_MW	Biomass electricity generation. (Float)
POWER_NGEM_EMBEDDED_SOLAR_GENERATION_MW	Embedded solar electricity generation. (Float)
POWER_NGEM_EMBEDDED_WIND_GENERATION_MW	Embedded wind electricity generation. (Float)
POWER_NGEM_BRITNED_FLOW_MW	BritNed electricity flow. (

File: espeni_raw.csv

Column name	Description
ELEXM_SETTLEMENT_DATE	Date of the settlement period. (Date)
ELEXM_SETTLEMENT_PERIOD	Half-hourly period of the settlement. (Integer)
ELEXM_utc	Time in UTC format. (Time)
ELEXM_localtime	Time in local time format. (Time)
ELEXM_ROWFLAG	Flag indicating the validity of the data. (Integer)
NGEM_ROWFLAG	Flag indicating the validity of the data. (Integer)
POWER_ESPENI_MW	Total electricity demand in the UK. (Float)
POWER_ELEXM_CCGT_MW	Combined cycle gas turbine electricity generation. (Float)
POWER_ELEXM_OIL_MW	Oil electricity generation. (Float)
POWER_ELEXM_COAL_MW	Coal electricity generation. (Float)
POWER_ELEXM_NUCLEAR_MW	Nuclear electricity generation. (Float)
POWER_ELEXM_WIND_MW	Wind electricity generation. (Float)
POWER_ELEXM_PS_MW	Pumped storage electricity generation. (Float)
POWER_ELEXM_NPSHYD_MW	Non-pumped storage hydro electricity generation. (Float)
POWER_ELEXM_OCGT_MW	Open cycle gas turbine electricity generation. (Float)
POWER_ELEXM_OTHER_POSTCALC_MW	Other electricity generation. (Float)
POWER_ELEXM_BIOMASS_POSTCALC_MW	Biomass electricity generation. (Float)
POWER_NGEM_EMBEDDED_SOLAR_GENERATION_MW	Embedded solar electricity generation. (Float)
POWER_NGEM_EMBEDDED_WIND_GENERATION_MW	Embedded wind electricity generation. (Float)
POWER_NGEM_BRITNED_FLOW_MW	BritNed electricity flow. (

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Espeni

@kaggle.thedevastator_gb_electrical_grid_half_hourly_data_2008_present.espeni

7.48 MB
246677 rows
26 columns


CREATE TABLE espeni (
  "elexm_settlement_date" TIMESTAMP,
  "elexm_settlement_period" BIGINT,
  "elexm_utc" VARCHAR,
  "elexm_localtime" VARCHAR,
  "elexm_rowflag" BIGINT,
  "ngem_rowflag" DOUBLE,
  "power_espeni_mw" BIGINT,
  "power_elexm_ccgt_mw" BIGINT,
  "power_elexm_oil_mw" BIGINT,
  "power_elexm_coal_mw" BIGINT,
  "power_elexm_nuclear_mw" BIGINT,
  "power_elexm_wind_mw" BIGINT,
  "power_elexm_ps_mw" BIGINT,
  "power_elexm_npshyd_mw" BIGINT,
  "power_elexm_ocgt_mw" BIGINT,
  "power_elexm_other_postcalc_mw" BIGINT,
  "power_elexm_biomass_postcalc_mw" BIGINT,
  "power_ngem_embedded_solar_generation_mw" DOUBLE,
  "power_ngem_embedded_wind_generation_mw" DOUBLE,
  "power_ngem_britned_flow_mw" DOUBLE,
  "power_ngem_east_west_flow_mw" DOUBLE,
  "power_ngem_moyle_flow_mw" DOUBLE,
  "power_ngem_nemo_flow_mw" DOUBLE,
  "power_ngem_ifa_flow_mw" DOUBLE,
  "power_ngem_ifa2_flow_mw" DOUBLE,
  "power_ngem_nsl_flow_mw" DOUBLE
);

Espeni Raw

@kaggle.thedevastator_gb_electrical_grid_half_hourly_data_2008_present.espeni_raw

7.5 MB
246677 rows
26 columns


CREATE TABLE espeni_raw (
  "elexm_settlement_date" TIMESTAMP,
  "elexm_settlement_period" BIGINT,
  "elexm_utc" VARCHAR,
  "elexm_localtime" VARCHAR,
  "elexm_rowflag" BIGINT,
  "ngem_rowflag" DOUBLE,
  "power_espeni_mw" BIGINT,
  "power_elexm_ccgt_mw" DOUBLE,
  "power_elexm_oil_mw" DOUBLE,
  "power_elexm_coal_mw" DOUBLE,
  "power_elexm_nuclear_mw" DOUBLE,
  "power_elexm_wind_mw" DOUBLE,
  "power_elexm_ps_mw" DOUBLE,
  "power_elexm_npshyd_mw" DOUBLE,
  "power_elexm_ocgt_mw" DOUBLE,
  "power_elexm_other_postcalc_mw" DOUBLE,
  "power_elexm_biomass_postcalc_mw" DOUBLE,
  "power_ngem_embedded_solar_generation_mw" DOUBLE,
  "power_ngem_embedded_wind_generation_mw" DOUBLE,
  "power_ngem_britned_flow_mw" DOUBLE,
  "power_ngem_east_west_flow_mw" DOUBLE,
  "power_ngem_moyle_flow_mw" DOUBLE,
  "power_ngem_nemo_flow_mw" DOUBLE,
  "power_ngem_ifa_flow_mw" DOUBLE,
  "power_ngem_ifa2_flow_mw" DOUBLE,
  "power_ngem_nsl_flow_mw" DOUBLE
);