Methodology and Quality Update

Latest Update on Methodology and Quality

25/11/2025

Statistical Presentation

Data description

The Household Energy Statistics present the data on the use of electricity sources. Household Energy Statistics and data on the uses of electricity sources, fuel uses, and consumption rates of different forms of energy in the dwelling, as well as identifying the means of rationalization and the extent of the household’s desire to use photovoltaic energy (solar) in the dwelling in Saudi Arabia.
The Household Energy Statistics cover the main characteristics as follows:

•     The main electricity sources used in the dwelling.
•     The main type of energy used for cooking. 
•     Use of steel gas cylinders.
•     Use of fiberglass gas cylinders. 
•     Use of thermal insulation in the dwelling.
•     Households interested in using solar energy in the dwelling.
•     Percentage of households that are very interested in rationalizing energy consumption in the dwelling.
•     Households following energy-saving practices for electrical appliances.
•     Households willing to replace old appliances with higher-efficiency ones.

Data is also used to estimate:
Sustainable development indicators related to the relative distribution of the population benefiting from electricity services.

Classifications

The following classifications are applied in Household Energy Statistics.
Standard International Energy Product Classification (SIEC):
It is a statistical classification endorsed by the United Nations for the standardized organization and classification of energy products, ensuring the consistency of energy statistics and their international comparability. It covers all products related to the production, transformation, and consumption of energy.
International Standard Energy Classification (SIEC)
Guide to Measuring Energy Access:
It is a guide for data collection using basic questions about household energy use. This reference is one of the global guides used in designing and implementing household energy surveys. This guide was jointly prepared by the World Bank and the World Health Organization (WHO), with support from the Energy Sector Management Assistance Program (ESMAP).
Guide to Measuring Energy Access

Statistical concepts and definitions

Terms and concepts of the Household Energy Statistics:
    Household energy consumption:
The energy consumed by the population for household purposes only (water heating, warming, air-conditioning, lighting, cooking).

• Fuel:

It refers to any type of material used to produce energy through a thermochemical reaction.

• Diesel:

It is a liquid hydrocarbon fuel obtained through the distillation of crude oil. It may be used in electric generators if the source of electricity is a private generator.

• Kerosene:

A flammable hydrogen liquid, often used as a fuel for cooking and also for lighting.

• Gas (cooking gas):

Cooking gas consists of a mixture of gases obtained from natural gas or from crude oil fractionation. It is used as a fuel for household heating and cooking and is commonly marketed in cylinders or tanks.

• Electrical energy:

The work required to move an electric charge through a conductor in a given 
time, and its unit is kilowatt-hours.

• Thermal insulation:

It refers to the use of materials with thermal-insulating properties during or after the construction phase in a way that helps reduce the transfer and leakage of heat from outside the building to the inside during summer and vice versa during winter.

• Photovoltaic energy (solar):

It refers to the energy generated using solar panels that convert solar radiation into electricity. The primary objective is to establish sufficient photovoltaic capacity to produce electrical power.

• Firewood

Firewood refers to all types of wood used and utilized as fuel.

•  Charcoal

A solid material composed mainly of carbon. It is produced through the destructive distillation of firewood in the absence of air.

• Agricultural Waste

It refers to the solid residues of tree fruits, such as the residues of olive fruits after pressing. These residues have various uses, including generating energy for cooking or heating purposes.

Data sources

Household Energy Statistics publication data are based on two sources:
First source: Household Energy Statistics Survey data.
 The disseminated key variables of survey data are:

•     Percentage of electricity sources in the household sector.
•     Percentage of fuel used for cooking in the household sector.
•     Percentage of households that are interested in using (solar) energy.
•     Percentage of households that are very interested in rationalizing energy consumption in the dwelling.
•     Percentage of households following energy-saving practices for electrical appliances.
•     Percentage of dwellings with thermal insulation.
•     Average operating hours of electrical appliances.
•     Percentage of dwellings using gas (cooking gas) by type (cylinder/tank).
•     Percentage of dwellings using gas cylinders by type (iron cylinder / fiber cylinder).
•     Percentage of households using biomass products.

Second source: Administrative record data from the following entities:
    Ministry of Energy.
The main published variables from the administrative data source are:

•     Percentage of population benefiting from electricity services.
•     Electricity consumption in the residential sector.

Designing the data collection tool

An electronic form (CAPI) was designed to ensure ease of use by field researchers, and the data was collected using a questionnaire prepared and designed by specialists at the General Authority for Statistics. During its design, international recommendations, standards, and definitions were taken into account, and it was also presented to relevant entities to gather their views and observations. The questions were formulated in a specific scientific manner to unify the format of question delivery by researchers.
Sections of the questionnaire:

•     Geographic identification data.
•     Section one: Basic household data.
•     Section two: Dwelling data.
•     Section three: Electricity use data.
•     Section four: Fuel use data.
•     Section five: Biomass use data.
•     Section six: Measuring the relationship between household energy consumption and the household’s monthly income.

Method of calculating the indicators:
Percentage of households that are very interested in rationalizing energy consumption in the dwelling =   *100

Percentage of households following energy-saving practices for
electrical appliances =  *100

Percentage of households interested in using solar energy in the
dwelling =  *100

Percentage of Households Using Electricity for Cooking =   *100

Percentage of households using gas (cooking gas) for cooking =   *100

Percentage of households using biomass products (firewood, charcoal, and agricultural residues) =   *100

Percentage of dwellings with thermal insulation =   *100

Review and Correction Rules:
 Audit and control rules have been established in the form to ensure that the data collected is consistent, accurate, and logical. These rules are designed to establish a logical relationship between answers and different questions and variables to help the researcher detect any errors directly when filling out the data with the household.
To ensure the quality of Household Energy Statistics Survey data, four types of review and correction rules were established, as follows:

•  Automated adjustment rules:

These rules are applied for the automatic calculation of certain fields or automatic adjustment of responses in specific fields to align with some questionnaires, totaling approximately 32 rules.

•   Navigation rules between sections and fields:

Special rules were programmed to regulate automatic navigation between sections and fields, based on the respondent’s input, totaling 124 rules.

•   Error rules:

These are rules that cannot be bypassed during the data entry process. The field researcher must correct the data by referring back to the respondent to verify its accuracy. The total number of these rules exceeds 300.

•   Alert rules (warnings):

These rules are designed to verify the correctness of the data entered by the researcher. The field researcher may override them if the data accuracy is confirmed, with a total of approximately 28 rules.
Administrative Household Energy Statistics data:
Administrative data are collected based on standardized data request forms that are sent to the data-providing entities to obtain periodic, harmonized, and documented data derived from administrative records related to household energy indicators. This process ensures improved data quality and promotes integration across the various data sources.
Household Energy Statistics questionnaire:
Household Energy Questionnaire

Questionnaire test (cognitive test)

Cognitive testing was conducted on a number of the questionnaire’s questions, based on the core pillars of cognitive testing. Several observations were recorded related to the following pillars: wording, comprehension, response options, and the measurement of disclosure feasibility. Accordingly, the final questionnaire was re-engineered.

Statistical population

The Household Energy Statistics product relies on data from the Household Energy Survey in addition to administrative records data. The statistical population of the Household Energy Survey consists of all households residing in the Kingdom of Saudi Arabia that occupy conventional dwellings. The existing household frame serves as the primary framework for the survey units, as it includes the classification of households by administrative regions, housing characteristics, and other essential basic data required for implementing household surveys. The 2022 Census data were used as the sampling frame for the target population.

 Sample Design

The 2025 Household Energy Survey sample was designed using a two-stage systematic stratified cluster random sampling method. In the first stage, a random sample of primary sampling units (enumeration areas) was selected for each stratum of the adopted sample design. In the second stage, a systematic random sample of housing units (households) is selected within each selected initial sampling unit.
Stratification
To increase the efficiency of the sample and its representation of the target population, the primary sampling units within the sample frame were classified into homogeneous strata to obtain more accurate results compared to a simple random sampling method of the same size; the division was carried out using governorates as actual strata due to the need to produce survey indicators separately for each governorate, as the number of governorates is 150.
Sample size and allocation across strata:
The total sample size was estimated at the kingdom level, and then the sample was distributed to the thirteen administrative regions using proportional to size allocation, while adjusting the sample size for small governorates so that it does not fall below a certain value.
The total sample size amounted to 27,481 households. The sample size was calculated using the following parameters and determinants:

•      The allowable coefficient of variation used in calculating the sample size was 1% at the national level and 4% at the administrative region level. 
•     The design effect of the sample used ranged between 0.336 and 3.514.
•     The expected response rate is 70%.
•     A confidence level of (1–α) = 0.95, which is used in determining the proportion to be estimated from the survey.

Table1: Distribution of the sample at the level of administrative regions:
 

Statistical unit (sampling unit)

The statistical unit in the Household Energy Statistics Survey is the household.

Data collection

Data collection from the survey:
Household Energy Statistics data are collected through computer-assisted telephone interviews (CATI) and computer-assisted personal interviews (CAPI).
Data collection from administrative records:
In coordination with the relevant GASTAT departments responsible for survey implementation and the Data Collection Department, the administrative data for the Household Energy Statistics publication are obtained from the Ministry of Energy. These data include the amount of electricity consumption in the residential sector and the percentage of the population benefiting from electricity services. 
The data is stored in the authority's databases after undergoing auditing and review processes following approved statistical methods and recognized quality standards. If errors or discrepancies are discovered, the data is cross-referenced with the data source for correction or clarification.

Data collection frequency 

The data collection process for Household Energy Statistics is carried out annually.

Reference area

The Household Energy Statistics cover 13 administrative regions in Saudi Arabia.

Reference period (time reference)

References period to the variables or dataset as follows:

•     The data for the Household Energy Statistics 2024 is based on the period from January 1st to December 31st.
•     The identifying household data, housing characteristics, and household information are referenced to the date of the household visit.  
•     Household Energy Statistics data are referenced to fuel use and electricity use data for the previous reference year, 2024.
•     Data from administrative records are based on the last day of each calendar year.

Base period

Not applicable.

Measurement unit

•  Most results are measured by numbers (such as the number of cylinders, amount of electricity consumption).
•  Some results are calculated as percentages (such as the percentage of dwellings with thermal insulation).

 Time coverage

The data is available from the year 2017 to 2024.

Publication frequency

The results of the Household Energy Statistics are published annually according to the approved statistical plan.

Statistical processing

Error detection

Accurate procedures are carried out to detect errors in the data collected during the field survey and stored in the data lake. This is achieved through the automation of the data collection tool and the implementation of the necessary constraints and procedures to control and manage the entered data, ensuring quality, accuracy, and consistency. Additionally, supporting methods are used to measure quality indicators, such as the survey response rate. These procedures include the following:

•     Identifying illogical, out-of-range, or contradictory data.
•     Detecting missing or incomplete data and handling them according to established policies.
•     Verifying internal consistency among the questionnaire responses.
•     Reviewing and matching data to ensure their accuracy and precision in a manner suitable to their nature, to enhance the quality and accuracy of the statistics presented.
•     Comparing the current publication’s data with the previous year’s data to ensure their integrity and consistency in preparation for data processing, result extraction, and review.
•     Processing and tabulating data to verify their accuracy and completeness.

All outputs are stored and uploaded to the database after being calculated by GASTAT, to be reviewed and processed by specialists in the Environment and Natural Resources Statistics Department using modern technologies and software designed for this purpose.

Data integration and matching from multiple sources 

Data from multiple sources were integrated and matched using two main sources to produce Household Energy Statistics, to ensure data integration and enhance accuracy and comprehensiveness. This is achieved by utilizing administrative records from the Ministry of Energy and linking them with the statistical data produced by the General Authority for Statistics. These data are processed in an integrated manner to ensure the completeness and accuracy of the statistical outputs.
The Household Energy Statistics rely on two main sources:

•     Administrative record data from relevant entities
•     Statistical survey data.

The process of data matching and consistency verification is carried out through several steps: 

•     Checking for duplication or variation in values.
•     Comparing the common variables (such as consumption quantities).
•     Resolving discrepancies by giving priority to the most accurate and comprehensive data.

This procedure aims to ensure the reliability and accuracy of the final data used in preparing the publication and to provide a clear and unified picture of Household Energy Statistics.

Imputation and calibration

Compensation (for non-response cases or incomplete datasets): 
Cases of non-response:
The response is analyzed at the level of the fully completed sample, after which a weight is estimated for each sampling unit to generalize the results to the entire population.
Incomplete data sets:
The General Authority for Statistics uses statistical methods to treat anomalous values and some missing data within the sections of the Household Energy Statistics form, such as using measures of central tendency at the level of the targeted strata.
Extrapolation and weighting: 
After processing the data collected from respondents, survey weights were generated to produce indicator tables by following two main steps in creating survey weights: 

•     Adjustment of non-response.
•     Calibration weight

Procedures for calculating variables and aggregates:
GASTAT relied on internationally accepted equations, in accordance with international standards, to calculate the main indicators of Household Energy Statistics, as follows:

•     By region: The totals by administrative region were calculated by summing the responses by variable for the entire region.
•     By type of dwelling: The totals by administrative region were calculated by summing the responses by variable for the entire region.
•     By type of housing tenure: The totals by administrative region were calculated by summing the responses by housing tenure type for the variable over the total number of tenure types in the region.

Seasonal adjustments

Not applicable, only final results will be published.

 Adjustment of preliminary results 

Not applicable. The results are published in their final form and are not released as preliminary results.

Used Resources

Quality dimensions

Suitability

A standard that measures the extent to which the product meets the needs of users.

User needs 

The Household Energy Statistics product aims to provide basic and structured data on household energy consumption and build a reliable database that supports decision makers and researchers. It also contributes to preparing studies and conducting local, regional, and international comparisons to develop this vital sector.
GASTAT's internal users of Household Energy Statistics data:

•     International indicators department.
•     Data management 

Several external users and beneficiaries who greatly rely on Household Energy Statistics data, including:

•     Government entities.

GCC Statistical Center        All the variables
    Research institutions.
    Media.
    Individuals.

Completeness 

A comprehensive review of data from various sources was conducted to ensure its completeness and compliance with national requirements and international standards, including SDG indicators and other relevant metrics. The review aimed to ensure the accuracy and completeness of the data and its alignment with international standards.
The publication includes the following key elements: 

•      The main electricity sources used in the dwelling.
•      The main type of energy used for cooking in the dwelling.
•     Households interested in using solar energy.
•     Households that are very interested in rationalizing energy consumption in the dwelling.
•     Percentage of households that apply the instructions for energy conservation in the use of electrical appliances. 
•     Dwellings with thermal insulation.

Accuracy and reliability 

A standard that measures how close the calculations or estimates are to the exact or true values that reflect reality.

Overall accuracy 

• The data collected is improved through the researchers who have been selected according to a set of practical and objective criteria and a training program related to the field of work.
•     Alert rules, blocking rules, and correction rules are applied during the data collection process on the electronic Household Energy Statistics questionnaire to improve data quality.
•     Data is checked with previous years to identify any significant changes in the data.
•     The internal consistency of the data is checked before it is finalized.
•     The links between variables are checked, and coherence between different data series is confirmed.

Timeliness and punctuality 

A standard that measures the time gap between the availability of information and the occurrence of the event.
However, timeliness reflects the time difference between the date of data publication and the target date when it is actually published.

Timeliness 

The General Authority for Statistics is committed to applying internationally recognized standards regarding the announcement and clarification of the time of publishing statistics on its official website, as outlined in the statistical calendar, as well as adhering to the announced time of publication. In the event of any delay, updates will be provided accordingly.

Punctuality 

The publication takes place according to the published release dates on the statistical calendar for Household Energy Statistics on the website of the General Authority for Statistics.
The data are available at the expected time, as scheduled in the statistical release calendar. If the publication is delayed, reasons shall be provided.

Coherence and comparability

Statistics should be consistent internally and over time, and logically interconnected across scope and statistical domains, meaning that data should be comparable across regions and countries as well as across different time periods for the same region, and data from diverse sources can be combined and used interchangeably.

Comparability – geographical

Statistical data related to household energy are fully comparable geographically across the administrative regions of the Kingdom, as well as at the regional and international levels. This is based on the standards adopted in compiling household energy datasets, which are built on internationally approved concepts, definitions, and classifications issued by the relevant authorities.
In addition, the geographical distribution of the administrative regions has not undergone any changes during the reference period, ensuring the stability of spatial comparisons and preventing any impact on the key indicators or their related variables.

Comparability - over time 

The statistics began in 2017 as an annual survey, and the main changes that have occurred in recent years are as follows:

• 2017–2019:

The survey was conducted using computer-assisted personal interviews (CAPI). 

•  2020:

The survey was not conducted, and reliance was mainly on statistical estimates derived from the historical Household Energy Survey data due to the circumstances of the COVID-19 pandemic. (COVID-19).

•  2021–2023:

The survey was conducted using computer-assisted telephone interviews (CATI). 

•  2024:

The survey was carried out relying on computer-assisted telephone interviews (CATI) and computer-assisted personal interviews (CAPI).

Coherence- Cross domain

Household Energy Statistics data undergo standardized verification and processing procedures. Data consistency is ensured by comparing it with data published in other statistical releases, such as Energy Efficiency Statistics and Electricity Energy Statistics, to ensure the alignment of indicators and avoid any discrepancies across statistical publications.

Coherence- Sub-annual and annual statistics 

Not applicable, as the Household Energy Statistics are published only as an annual publication.

Coherence- National Accounts 

Household Energy Statistics are integrated with national accounts requirements through the adoption of energy statistical classifications, such as the Standard International Energy Classification (SIEC). The results of the publication — particularly consumption quantities — are used as key inputs for estimating the contribution of the electricity sector to the Gross Domestic Product (GDP) within the national accounts framework. Continuous coordination is maintained with the National Accounts Statistics team to ensure consistency between the publication’s results and national accounts outputs.

Coherence- Internal 

The Household Energy Statistics publication is internally consistent, with data within each dataset aligning logically and ensuring consistency across different measures, such as totals, quantities, and percentages.
Internal consistency is verified through:

•     Ensuring that data logically align with one another within the overall context of the bulletin.
•     Matching totals with detailed data by type and administrative region.
•     Reviewing the relationships between indicators and variables, such as consumption quantities, number of gas refills, appliances used, and sources of energy types used in the housing unit.

 Accessibility and clarity

The ability for users to access data, the availability of accurate or complete data, and the availability of a methodology and quality report.

Press releases

The announcements for each publication are available on the statistical calendar as mentioned in 10.1. The press releases can be viewed on the website of GASTAT on the link: 
Press release

Publications

GASTAT issues Household Energy Statistics Publications and Reports regularly within a pre-prepared dissemination plan, and they are published on GASTAT’s website.  GASTAT is keen to publish its publications in a way that serves all users of different types, including publications in different formats that contain publication tables, data graphs, indicators, metadata, methodology, and questionnaires in both English and Arabic.
The results of the Household Energy Statistics are available at:
  Household Energy Statistics

Online database

The data is published on the statistical database:
Data confidentiality at the General Authority for Statistics

Microdata accessibility

Accurate data is unit-level disaggregated data obtained from multiple sources such as sample statistical surveys, general population and housing censuses, and administrative systems, providing detailed information about the characteristics of individuals, families, business entities, and geographical areas, supporting the construction and development of statistical indicators and scientific research.
Different types of microdata files to meet diverse information needs:

•   Public use:

‌It consists of sets of records containing information on individuals, households, or business entities anonymized in such a way that the respondent cannot be identified either directly (such as by name, address, contact number, identity number, etc.) or indirectly (by combining different – especially rare – characteristics of respondents), such as age, occupation, education, etc.

•   Scientific use:

These files are established based on a specific methodology requested by the data requester to extract the datasets with specific characteristics used for strategic studies and decision-making, as well as scientific research purposes on individuals, households, and enterprises with no direct identifiers, which have been subject to control methods to protect confidentiality.
Qualified users who meet the standards and procedures of confidentiality protection can access the files of scientific use of accurate data through the platform "ITAHA" of the General Authority for Statistics, while the most sensitive data for use is shared by visiting the accurate data laboratory within a secure environment managed by the Authority.

References and standards

Household Energy Statistics Framework: 
GASTAT carries out all its statistical work according to a unified methodology that aligns with the nature of each statistical product. This methodology is based on the Generic Statistical Business Process Model (GSBPM), which is consistent with the operational procedures adopted by international organizations and harmonized with the practices of the relevant national entities.
For more details, you can refer to the attachment. 
Generic Statistical Business Process Model (GSBPM)
International Recommendations for Energy Statistics (IRES):
Concepts, definitions, issues, and classifications are aligned with the international recommendations for energy statistics adopted by the United Nations Statistics Division.
International Recommendations for Energy Statistics (IRES)

The Guidelines for Completing the Questionnaire (Researcher Handbook) were adopted as the main reference for data collection teams and were officially used during the survey implementation. The handbook provides clear instructions for filling out the questionnaire, comprehensive explanations of the concepts and definitions used, and guidance for handling various field situations, ensuring data quality, accuracy, and consistency with survey standards.
Furthermore, the calculation of Indicator 7.1.1 is based on the methodology issued by the United Nations Statistics Division (UNSD).  

SDG Indicators   

Quality assurance

GASTAT declares that it considers the following principles: Impartiality, ensuring that the statistical product is user-oriented, maintaining the quality of processes and outputs, enhancing the effectiveness of statistical operations, and reducing the burden on respondents. 
Data is validated through procedures and quality controls that are applied during the process at various stages, such as: (data entry, data collection, and other final controls).

Quality assessment

GASTAT performs all statistical activities according to a national model (Generic Statistical Business Process Model – GSBPM). According to the GSBPM, the final phase of statistical activities is overall evaluation using information gathered in each phase or sub-process. This information is used to prepare the evaluation report, which outlines all the quality issues related to the specific statistical activity and serves as input for improvement actions.

Confidentiality

Confidentiality - Policy

According to Royal Decree No. 23 dated 07/12/1379, data must always be kept confidential and must be used by GASTAT for statistical purposes only.
Therefore, the data is protected in the data servers of GASTAT.

Confidentiality - Data Treatment

Data from the SMEs survey are presented in the right tables in order to summarise and understand as well as extract their results. Moreover, to compare them with other data and to obtain statistical significance about the selected study population. However, referring to such data indicated in tables is much easier than going back to check the original questionnaire, which may include some data like names and addresses of individuals and names of data providers, which violates the data confidentiality of statistical data.
“Anonymity of data” is one of the most important procedures. To keep data confidential,
GASTAT removed information on individual persons, households, or business entities in such a way that the respondent cannot be identified either directly (such as by name, address, contact number, identity number, etc.) or indirectly (by combining different, especially rare characteristics of respondents), such as age, occupation, education, etc.

Dissemination policy

Statistical calendar

Household Energy Statistics are included in the statistical calendar.
Statistical Calendar

User access

One of the objectives of the General Authority for Statistics is to better meet the needs of its users; therefore, the results of the Household Energy Statistics publication are made available to all users immediately upon release.
It also receives questions and inquiries from clients about the publication and its results through various communication channels, such as:
    GASTAT official website: www.stats.gov.sa
    GASTAT official email address: info@stats.gov.sa
    Official visits to GASTAT’s official head office in Riyadh or one of its branches in Saudi Arabia.
    Official letters.
    Statistical telephone: (199009).