This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This layer shows real time Statewide percentage of customers without power. Last Updated: Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/UtilityTelecom/MD_PowerOutages/MapServer/0 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Source

https://ourworldindata.org/energy-access

Description

The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. The database covers approximately 35,000 power plants from 167 countries and includes thermal plants (e.g. coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g. hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. It will be continuously updated as data becomes available.

The methodology for the dataset creation is given in the World Resources Institute publication "A Global Database of Power Plants". Data updates may occur without associated updates to this manuscript.

The database can be visualized on Resource Watch together with hundreds of other datasets.

Citation Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. 2018. Global Power Plant Database. Published on Resource Watch and Google Earth Engine; http://resourcewatch.org/ https://earthengine.google.com/

This file ‘all_areas_dataframe_renewables_and_non_renewables.xlsx’ is the result of the notebook https://www.kaggle.com/code/fords001/renewable-and-non-renewable-electricity-resources . It contains information from the years 2000 to 2023 and includes 18 sheets: for the percentage of electricity generation and for electricity generation in terawatt-hours (TWh) for each of the following world regions: Africa, Europe, Asia, North America, Latin America and the Caribbean, Oceania, as well as for the entire world. Each region has 11 columns representing different sources of electricity generation: Non-Renewables: Coal, Gas, Nuclear, Other Fossil (4 columns), Renewables: Bioenergy, Hydro, Solar, Wind, Other Renewables (5 columns). For each world region, we have two additional columns: Total Non-Renewables (1 column) and Total Renewables (1 column), which will be the sum of the related electricity generation columns .

List of dataframes : 'All_Areas_Common_Percent ' - Percentage dataframe for all areas 'All_Areas_Common_TWh' - Terawatt-hours dataframe for all areas 'All_Areas_Percent_Ren_Non_R' - Percentage df for all areas for 2 columns(Non-Renewables , Renewable) 'All_Areas_TWh_Ren_and_Non_R' - TWh df for all areas for 2 columns(Non-Renewables , Renewable) 'World_DF_Percent' - World dataframe Percentage 'World_DF_TWh' - World dataframe Terawatt-hours 'Africa_DF_Percent' - Africa dataframe Percentage 'Africa_DF_TWh' - Africa dataframe Terawatt-hours 'Europe_DF_Percent' - Europe dataframe Percentage 'Europe_DF_TWh' - Europe dataframe Terawatt-hours 'Asia_DF_Percent' - Asia dataframe Percentage 'Asia_DF_TWh' - Asia dataframe Terawatt-hours 'North_America_DF_Percent' - North America dataframe Percentage 'North_America_DF_TWh' - North America dataframe Terawatt-hours 'Latin_America_and_C_DF_Percent' - World dataframe Percentage 'Latin_America_and_C_DF_Twh' - World dataframe Terawatt-hours 'Oceania_DF_Percent' - Oceania dataframe Percentage 'Oceania_DF_TWh' - Oceania dataframe Terawatt-hours

In this data analysis I used the dataset ‘yearly_full_release_long_format.csv’, from https://ember-energy.org/data/yearly-electricity-data/ .It has a license (Creative Commons Attribution Licence (CC-BY-4.0). This license means. Share — copy and redistribute the material in any medium or format for any purpose, even commercially. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. These are the links to the license description . https://ember-energy.org/creative-commons/ and https://creativecommons.org/licenses/by/4.0/

1.National/Regional Policies 1.1 Paris Agreement Ratification I believe Paris Agreement has fostered the ambition of the countries to revise and implement their climate goals. However, not all of them have ratified it. I think the countries which have not ratified the agreement might show a different direction/ or they might be less ambitious in putting climate change adaptation and mitigation into their political agenda/processes/actions.

Data Source: https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XXVII-7-d&chapter=27&clang=_en

1.2 Climate Change ambitions While the majority of countries have promised to take action against climate change through the Paris Agreement, not all of them are working towards reaching the 1.5C goal at the same level. Climate tracker is an organization tracking the “ambition level” and progress of the countries which I believe could be a fruitful source of data.

Overview: https://climateactiontracker.org/publications/paris-agreement-benchmarks/ Data Source: https://climateactiontracker.org/data-portal/

1.3 Carbon Pricing Some countries/regions implement carbon pricing mechanism which is proven to be an efficient mechanism for decreasing carbon emissions. Worldbank provides a dashboard with carbon pricing data and information about the countries. Overview: https://carbonpricingdashboard.worldbank.org/ Data source: https://carbonpricingdashboard.worldbank.org/map_data

2. Economy 2.1 Composition of the sectors I know it is already shared by others, but the World Bank also provides further information on countries’ economy structures. One thing that I believe could be useful further to the GDP is the sector composition of the country which could play a role in countries' emission reduction. While it is easier for services to reach net-zero, it is harder for manufacturing. (this is also valid for companies, it is much easier to reach net-zero emission for a service company, but it could be very difficult for a steel production/processing plant to be emission-free). Overview: https://data.worldbank.org/indicator/NV.IND.MANF.ZS Data source: http://wdi.worldbank.org/table/4.2

2.2 Innovation Index Combating climate change requires fundamental changes in the systems that we have been living. Thus, innovation (technological, business model, political, social…) is necessary at all levels. Therefore, I believe the Global Innovation Index (GII) can be used as a proxy to measure innovative activities. Overview: https://www.globalinnovationindex.org/home Data source: https://www.globalinnovationindex.org/analysis-indicator

3. Low carbon Technologies Development, production and adoption of clean energy technologies are vital for lower carbon transitions. While latest developments in solar technologies made it both the cheapest and clean energy source, there is still a long way to reach a “reliable” technology to be considered as a commercially feasible option for Carbon Capture and Storage. IEA provides information related to low carbon RDDs, but it has limited country data (Mostly OECD countries). Overview: https://www.iea.org/fuels-and-technologies Data Source: https://www.iea.org/reports/energy-technology-rdd-budgets-2020

4. Development & Just Transitions 4.1 Energy Access Today there are still millions of people who don’t have access to electricity and clean cooking. Although for some countries finding ways to decrease emissions, for some others to ensure their population’s “reliable affordable and clean energy access” (SDG 7, UNDP) is the challenge. The World Bank provides data on Electricity production, sources, and percentage of the population who has access to electricity by country as part of World Development Indicators. Overview: https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS Data Source: http://wdi.worldbank.org/table/3.7

4.2. Bonus: Environmental Justice (no dataset uploaded- just qualitative data) Environmental Justice Atlas is a citizen-led mapping tool which shows conflicts related to environmental injustices. The data cannot be fully downloaded and subject to restrictive data use, and I am not sure even if it could be quantified. But, I believe it could be useful to think about the social aspects of transitions. https://ejatlas.org/

The dataset contains state- and region-wise NSS 78th round compiled data on Percentage of Households who have No Cooking Arrangement and Percentage of Households using Primary Sources of Energy such as Firewood, LPG, Dung Cake, Gobar Gas, etc. for Cooking

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe has further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors, such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels, determine the amount of electricity the average person requires in each country.

About this dataset

Context

The dataset tabulates the population of Power County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Power County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.05% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Power County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Power County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Power County Population by Gender. You can refer the same here

Please cite as "2020 COVID19 Global Daily Impact Dataset by criticalperegrine.tumblr" Please read the .ods file for sources

Purpose

A dataset containing statistics pertaining to : * Policy - what special mesures were applied during the year * Epidemic - how fast is COVID spreading, how deadly it is, how much has it spread & killed people * Population - How many people per country, how old they are, how urban and concentrated they are * Medical System - How many Physicians & Beds exist * Weather - Temperature, Humidity and Wind * Electrical Grid - How has the consumption of electricity changed * Aviation - How have the number of flights varied The reader can view the detailed sources for each statistic in "fullCOVIDsources.ods" with precise links / citations. The .csv dataset itself can be opened with Excel or any spreadsheet program. Wunderground.com was used for (almost) all Weather data. The Oxford Government response tracker was used for all Policy data.

Specific Rationales

Reff

The "Epidemic" Statistics contain the Reff, a measure of the propagation of the epidemic. This was computed through the "EpiEstim" package by Cori et al (https://pbil.univ-lyon1.fr/CRAN/web/packages/EpiEstim/index.html), through the used of the serial interval by Challen et al ( https://www.medrxiv.org/content/10.1101/2020.11.17.20231548v2 ). The choice behind this serial interval is due to the fact that it reportedly accounts for pre-symptomatic transmission, an important feature according to the literature, whilst showing similar Reff for most regions as a more cited distribution by Qun Li et al ( https://www.nejm.org/doi/full/10.1056/NEJMOa2001316 ). The reader can inspect the code that generates the Reff values by reading the file "Reff Computation.r". The choice behind the Reff itself is because it is a simple to interpret indicator : >1, we have an epidemic; <1, we do not.

Rationale for "Electrical Grid"

Electricity is used in most of the world, save for very very rural countries, for personal & industrial use. From cooking food, to transforming goods through the use of heavy machinery, to services (digital, or simply powering the light in venues providing services). It is essential for production, and a major decrease in consumption in electricity would imply a decrease in "daily" GDP (Gross Domestic Production) since : * Electricity is difficult to stock, so most electrical demand is related to needs for that day * There is no reported "major innovation" that decreases electricity consumption by more than 10% whilst maintaining a country's production * Electricity is used to transform most goods and produce most services in a country, as mentioned previously. So electricity is used to compare the shock done to the GDP due to different policies or infection rates.

Rationale for "Aviation"

Arrivals were used to make the effect of "Closed Borders" pop out. Aviation is used here as a non essential good, and also as a measure of international mobility throughout the year

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 33kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Grid Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Grid Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power NetworksTo view this data please register and login.

This dataset contains 2,000 records and 11 columns, capturing key operational and descriptive attributes related to power grid technology projects. It integrates information on project identifiers, components, system types, electrical parameters, equipment conditions, communication-quality indicators, issue categories, severity levels, and a target event label indicating whether an operational scenario is critical or normal. The dataset supports analysis of relationships among project activities, grid components, system performance, and event occurrences within power grid environments. It offers a structured foundation for exploring behavior patterns, equipment interactions, operational conditions, and event-based interpretations across diverse power grid scenarios.

Key Features:

Project_ID – Unique identifier assigned to each power grid project.

Component – Equipment or device involved in the project or event.

System_Name – Name of the system associated with monitoring or operational tasks.

Voltage_Level_kV – Measured operating voltage level in kilovolts.

Current_Amp – Amount of electric current flowing through the component in amperes.

Temperature_C – Operating temperature of the component in degrees Celsius.

Sensor_Packet_Loss_% – Percentage of data packets lost during sensor communication.

Power_Frequency_Hz – Recorded power system frequency in hertz.

Issue_Type – Type of operational or technical issue observed.

Severity_Index – Numeric scale (1–9) indicating how severe the issue or condition is.

Target_Event – Label indicating whether the event is normal (0) or critical (1).

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2023, figures for 2024 are revised provisional.

Changes as of November 2025: Figures have been revised from 2021 – 2022 and updated for 2023 -2024 The revision concerns improved data on (bio)diesel oil consumption by mobile equipment in the construction and services sectors. This results in a shift of biodiesel consumption in energy application transport to energy application heating and cooling. These changes amount to a few PJ.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 132 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross-referenced in the 132kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach

The dataset is not derived, it is the measurements from our network stored in our historian.

The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.

We developed a data redactions process to protect the privacy of companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.

The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.

Quality Control Statement

The data is provided "as is".

In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance Statement

Creating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets.

There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional Information

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary.

Download dataset information: Metadata (JSON)To view this data please register and login.

This dataset contains the monthly average on time performance (OTP) percentage by route and service day type (weekday, Saturday, and Sunday/Holiday service). A bus is considered on time if it is no more than one minute early or five minutes late to a timepoint.

Port Authority has an OTP goal of 73% for bus and 80% for rail service.

Starting in October 2018, Port Authority moved to a different OTP recording system called Clever. OTP data from the Clever system is more accurate because it uses more timepoints; the previous system excluded a large portion of data from OTP processing due to minor technical issues with rider counts on certain trips.

The Mon Incline is not included in this dataset because it does not have a schedule. Service runs every 15 minutes.

OTP only goes back as far as November 2018 for the "T" light rail line because the railcars did not have Automated Vehicle Locators installed until then.

This table contains information about the Dutch production of renewable electricity, the number of installations used and the installed capacity of these installations. During production, a distinction is made between normalised gross production and non-standard gross and net production without normalisation.

Production of electricity is shown in million kilowatt hours and as a percentage of total electricity consumption in the Netherlands. The production of renewable electricity is compared with total electricity consumption and not against total electricity production. This choice is due to European conventions.

The data is broken down according to the type of energy source and the technique used to obtain the electricity. A distinction is made between four main categories: hydro power, wind energy, solar power and biomass.

Data available from: 1990.

Status of the figures: This table contains definite figures until 2023, and revised provisional figures for 2024.

Changes as of November 2025: Figures for 2023 and 2024 have been updated.

Changes as of July 2025: Figures for the number of installations and capacity for solar electricity have been adjusted. Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the most recent data for number of solar installations and solar capacity, but outdated 2024 data on solar production. The most recent figures are 5 percent higher for 2024 solar electricity production. These most recent figures are available in these two tables (in Dutch): - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update of all StatLine tables covering solar production is scheduled in November 2025. From that moment all tables will be fully consistent again. We apologize for the inconvenience.

Changes as of June 6th 2025: Figures for 2024 have been updated.

Changes as of March 10th 2025: Figures added for 2024.

Changes as of January 2025: Figures on the capacities of municipal waste and biogas are added for 2022 and 2023.

Changes as of November 2024: Figures about capacity are now published.

Changes as of November 2024: Figures for 2021- 2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional.Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised. The capacity of solar photovoltaic from 2022 onwards is equal tot the system capacity of the installation. This means the maximal capacity with respect to the panel or the inverter.

When will new figures be published? Provisional figures on electricity output for the previous year are published each year in February. Revised provisional figures on electricity output for the previous year are published each year in June. Definite figures on electricity output for the previous year are published each year in December.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks To view this data please register and login.