The operation of residential buildings worldwide consumed more energy than every other segment of the real estate and construction sectors together in 2022. Non-residential buildings were responsible for the consumption of *** percent of all the energy used worldwide that year. Meanwhile, other construction activities, which is the segment that includes the construction of infrastructures, were responsible for over ***** percent of all energy consumption.

Energy Consumption of United States Over Time

Building Energy Data Book

By Department of Energy [source]

About this dataset

The Building Energy Data Book (2011) is an invaluable resource for gaining insight into the current state of energy consumption in the buildings sector. This dataset provides comprehensive data on residential, commercial and industrial building energy consumption, construction techniques, building technologies and characteristics. With this resource, you can get an in-depth understanding of how energy is used in various types of buildings - from single family homes to large office complexes - as well as its impact on the environment. The BTO within the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy developed this dataset to provide a wealth of knowledge for researchers, policy makers, engineers and even everyday observers who are interested in learning more about our built environment and its energy usage patterns

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How to use the dataset

This dataset provides comprehensive information regarding energy consumption in the buildings sector of the United States. It contains a number of key variables which can be used to analyze and explore the relations between energy consumption and building characteristics, technologies, and construction. The data is provided in both CSV format as well as tabular format which can make it helpful for those who prefer to use programs like Excel or other statistical modeling software.

In order to get started with this dataset we've developed a guide outlining how to effectively use it for your research or project needs.

• Understand what's included: Before you start analyzing the data, you should read through the provided documentation so that you fully understand what is included in the datasets. You'll want to be aware of any potential limitations or requirements associated with each type of data point so that your results are valid and reliable when drawing conclusions from them.

• Clean up any outliers: You may need to take some time upfront investigating suspicious outliers within your dataset before using it in any further analyses — otherwise, they can skew results down the road if not dealt with first-hand! Furthermore, they could also make complex statistical modeling more difficult as well since they artificially inflate values depending on their magnitude within each example data point (i.e., one outlier could affect an entire model’s prior distributions). Missing values should also be accounted for too since these may not always appear obvious at first glance when reviewing a table or graphical representation - but accurate statistics must still be obtained either way no matter how messy things seem!

• Exploratory data analysis: After cleaning up your dataset you'll want to do some basic exploring by visualizing different types of summaries like boxplots, histograms and scatter plots etc.. This will give you an initial case into what trends might exist within certain demographic/geographic/etc.. regions & variables which can then help inform future predictive models when needed! Additionally this step will highlight any clear discontinuous changes over time due over-generalization (if applicable), making sure predictors themselves don’t become part noise instead contributing meaningful signals towards overall effect predictions accuracy etc…

• Analyze key metrics & observations: Once exploratory analyses have been carried out on rawsamples post-processing steps are next such as analyzing metrics such ascorrelations amongst explanatory functions; performing significance testing regression models; imputing missing/outlier values and much more depending upon specific project needs at hand… Additionally – interpretation efforts based

Research Ideas

• Creating an energy efficiency rating system for buildings - Using the dataset, an organization can develop a metric to rate the energy efficiency of commercial and residential buildings in a standardized way.
• Developing targeted campaigns to raise awareness about energy conservation - Analyzing data from this dataset can help organizations identify areas of high energy consumption and create targeted campaigns and incentives to encourage people to conserve energy in those areas.
• Estimating costs associated with upgrading building technologies - By evaluating various trends in building technologies and their associated costs, decision-makers can determine the most cost-effective option when it comes time to upgrade their structures' energy efficiency...

The construction sector in the United Kingdom was responsible for consuming some ******* metric tons of oil equivalent renewable and waste energy in 2018. Renewable energy consumption had notably increased since 1990, when only ***** metric tons of renewable energy were used.

Content

This company produces several types of coils, steel plates, and iron plates. The information on electricity consumption is held in a cloud-based system. The information on energy consumption of the industry is stored on the website of the Korea Electric Power Corporation (pccs.kepco.go.kr), and the perspectives on daily, monthly, and annual data are calculated and shown.

Attribute Information:

Date Continuous-time data taken on the first of the month Usage_kWh Industry Energy Consumption Continuous kWh Lagging Current reactive power Continuous kVarh Leading Current reactive power Continuous kVarh CO2 Continuous ppm NSM Number of Seconds from midnight Continuous S Week status Categorical (Weekend (0) or a Weekday(1)) Day of week Categorical Sunday, Monday : Saturday Load Type Categorical Light Load, Medium Load, Maximum Load

Acknowledgements

This dataset is sourced from the UCI Machine Learning Repository Relevant Papers:

1. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city†, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.
2. Sathishkumar V E, Myeongbae Lee, Jonghyun Lim, Yubin Kim, Changsun Shin, Jangwoo Park, Yongyun Cho, “An Energy Consumption Prediction Model for Smart Factory using Data Mining Algorithms†KIPS Transactions on Software and Data Engineering, Vol. 9, no. 5, pp. 153-160, 2020. Transactions on Software and Data Engineering, Vol. 9, no. 5, pp. 153-160, 2020.
3. Sathishkumar V E, Jonghyun Lim, Myeongbae Lee, Yongyun Cho, Jangwoo Park, Changsun Shin, and Yongyun Cho, “Industry Energy Consumption Prediction Using Data Mining Techniques†, International Journal of Energy Information and Communications, Vol. 11, no. 1, pp. 7-14, 2020.

Inspiration

Which times of the year is the most energy consumed? What patterns can we identify in energy usage?

In the fiscal year 2022, the agriculture, mining, and construction sector in Japan consumed final energy of approximately 343 petajoules, down from around 388 petajoules in fiscal year 2013. Within the industrial sector, it had the smallest final energy consumption.

United Kingdom Energy Consumption: Construction data was reported at 725.200 TOE th in 2017. This records an increase from the previous number of 685.060 TOE th for 2016. United Kingdom Energy Consumption: Construction data is updated yearly, averaging 726.190 TOE th from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 1,150.000 TOE th in 1992 and a record low of 528.150 TOE th in 2007. United Kingdom Energy Consumption: Construction data remains active status in CEIC and is reported by Department for Business, Energy and Industrial Strategy. The data is categorized under Global Database’s United Kingdom – Table UK.RB007: Energy Consumption: by Industrial Consuming Group (Annual).

Indonesia Energy: Consumption: Industry & Construction: Iron and Steel Industry: Electricity data was reported at 9,731.000 TJ in 2017. This records a decrease from the previous number of 18,223.000 TJ for 2016. Indonesia Energy: Consumption: Industry & Construction: Iron and Steel Industry: Electricity data is updated yearly, averaging 14,209.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 21,991.000 TJ in 2006 and a record low of 4,842.000 TJ in 2014. Indonesia Energy: Consumption: Industry & Construction: Iron and Steel Industry: Electricity data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

This table contains figures on the supply and consumption of energy broken down by sector and by energy commodity. The energy supply is equal to the indigenous production of energy plus the receipts minus the deliveries of energy plus the stock changes. Consumption of energy is equal to the sum of own use, distribution losses, final energy consumption, non-energy use and the total net energy transformation. For each sector, the supply of energy is equal to the consumption of energy.

For some energy commodities, the total of the observed domestic deliveries is not exactly equal to the sum of the observed domestic receipts. For these energy commodities, a statistical difference arises that can not be attributed to a sector.

The breakdown into sectors follows mainly the classification as is customary in international energy statistics. This classification is based on functions of various sectors in the energy system and for several break downs on the international Standard Industrial Classification (SIC). There are two main sectors: the energy sector (companies with main activity indigenous production or transformation of energy) and energy consumers (other companies, vehicles and dwellings). In addition to a breakdown by sector, there is also a breakdown by energy commodity, such as coal, various petroleum products, natural gas, renewable energy, electricity and heat and other energy commodities like non renewable waste.

The definitions used in this table are exactly in line with the definitions in the Energy Balance table; supply, transformation and consumption. That table does not contain a breakdown by sector (excluding final energy consumption), but it does provide information about imports, exports and bunkering and also provides more detail about the energy commodities.

Data available: From: 1990.

Status of the figures: Figures up to and including 2022 are definite. Figures for 2023 and 2024 are revised provisional.

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 updated.

Changes as of March 17th 2025: For all reporting years the underlying code for 'Total crudes, fossil fraction' and 'Total kerosene, fossiel fraction' is adjusted. Figures have not been changed.

Changes as of November 15th 2024: The structure of the table has been adjusted. The adjustment concerns the division into sectors, with the aluminum industry now being distinguished separately within the non-ferrous metal sector. This table has also been revised for 2015 to 2021 as a result of new methods that have also been applied for 2022 and 2023. This concerns the following components: final energy consumption of LPG, distribution of final energy consumption of motor gasoline, sector classification of gas oil/diesel within the services and transfer of energy consumption of the nuclear industry from industry to the energy sector. The natural gas consumption of the wood and wood products industry has also been improved so that it is more comparable over time. This concerns changes of a maximum of a few PJ.

Changes as of June 7th 2024: Revised provisional figures of 2023 have been added.

Changes as of April 26th of 2024 The energy balance has been revised for 2015 and later on a limited number of points. The most important is the following: 1. For solid biomass and municipal waste, the most recent data have been included. Furthermore data were affected by integration with figures for a new, yet to be published StatLine table on the supply of solid biomass. As a result, there are some changes in receipts of energy, deliveries of energy and indigenous production of biomass of a maximum of a few PJ. 2. In the case of natural gas, an improvement has been made in the processing of data for stored LNG, which causes a shift between stock changes, receipts of energy and deliveries of energy of a maximum of a few PJ.

Changes as of March 25th of 2024: The energy balance has been revised and restructured. This concerns mainly the following: 1. Different way of dealing with biofuels that have been mixed with fossil fuels 2. A breakdown of the natural gas balance of agriculture into greenhouse horticulture and other agriculture. 3. Final consumption of electricity in services

1. Blended biofuels Previously, biofuels mixed with fossil fuels were counted as petroleum crude and products. In the new energy balance, blended biofuels count for renewable energy and petroleum crude and products and the underlying products (such as gasoline, diesel and kerosene) only count the fossil part of mixtures of fossil and biogenic fuels. To make this clear, the names of the energy commodities have been changed. The consequence of this adjustment is that part of the energy has been moved from petroleum to renewable. The energy balance remains the same for total energy commodities. The aim of this adjustment is to make the increasing role of blended biofuels in the Energy Balance visible and to better align with the Energy Balances published by Eurostat and the International Energy Agency. Within renewable energy, biomass, liquid biomass is now a separate energy commodity. This concerns both pure and blended biofuels.

2. Greenhouse horticulture separately The energy consumption of agriculture in the Netherlands largely takes place in greenhouse horticulture. There is therefore a lot of attention for this sector and the need for separate data on energy consumption in greenhouse horticulture. To meet this need, the agriculture sector has been divided into two subsectors: Greenhouse horticulture and other agriculture. For the time being, we only publish separate natural gas figures for greenhouse horticulture.

3. Higher final consumption of electricity in services in 2021 and 2022. The way in which electric road transport is treated has improved, resulting in an increase in the supply and final consumption of electricity in services by more than 2 PJ in 2021 and 2022. This also works through the supply of electricity in sector H (Transport and storage).

Changes as of November 14th 2023: Figures for 2021 and 2022 haven been adjusted. Figures for the Energy Balance for 2015 to 2020 have been revised regarding the following items: - For 2109 and 2020 final consumption of heat in agriculture is a few PJ lower and for services a few PJ higher. This is the result of improved interpretation of available data in supply of heat to agriculture. - During the production of geothermal heat by agriculture natural gas is produced as by-product. Now this is included in the energy balance. The amount increased from 0,2 PJ in 2015 to 0,7 PJ in 2020. - There are some improvements in the data for heat in industry with a magnitude of about 1 PJ or smaller. - There some other improvements, also about 1 PJ or smaller.

Changes as of June 15th 2023: Revised provisional figures of 2022 have been added.

Changes as of December 15th 2022: Figures for 1990 up to and including 2019 have been revised. The revision mainly concerns the consumption of gas- and diesel oil and energy commodities higher in the classification (total petroleum products, total crude and petroleum produtcs and total energy commodities). The revision is twofold: - New data for the consumption of diesel oil in mobile machine have been incorporated. Consequently, the final energy consumption of gas- and diesel oil in construction, services and agriculture increases. The biggest change is in construction (+10 PJ from 1990-2015, decreasing to 1 PJ in 2019. In agriculture the change is about 0.5-1.5 PJ from 2010 onwards and for services the change is between 0 and 3 PJ for the whole period. - The method for dealing with the statistical difference has been adapted. Earlier from 2013 onwards a difference of about 3 percent was assumed, matching old data (up to and including 2012) on final consumption of diesel for road transport based on the dedicated tax specifically for road that existed until 2012. In the new method the statistical difference is eliminated from 2015 onwards. Final consumption of road transport is calculated as the remainder of total supply to the market of diesel minus deliveries to users other than road transport. The first and second item affect both final consumption of road transport that decreases consequently about 5 percent from 2015 onwards. Before the adaption of the tax system for gas- and diesel oil in 2013 the statistical difference was positive (more supply than consumption). With the new data for mobile machines total consumption has been increased and the statistical difference has been reduced and is even negative for a few years.

Changes as of 1 March 2022: Figures for 1990 up to and including 2020 have been revised. The most important change is a different way of presenting own use of electricity of power-generating installations. Previously, this was regarded as electricity and CHP transformation input. From now on, this is seen as own use, as is customary in international energy statistics. As a result, the input and net energy transformation decrease and own use increases, on average about 15 PJ per year. Final consumers also have power generating installations. That's why final consumers now also have own use, previously this was not so. In the previous revision of 2021, the new sector blast

Final consumption of coal by the construction industry in the United Kingdom amounted to 10 thousand metric tons in 2022. This figure represents an increase of three thousand metric tons in comparison to the previous year.

Indonesia Energy: Consumption: Industry & Construction: Chemical Industry: Electricity data was reported at 36,569.000 TJ in 2017. This records an increase from the previous number of 7,919.000 TJ for 2016. Indonesia Energy: Consumption: Industry & Construction: Chemical Industry: Electricity data is updated yearly, averaging 14,461.000 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 47,646.000 TJ in 2013 and a record low of 5,832.000 TJ in 2015. Indonesia Energy: Consumption: Industry & Construction: Chemical Industry: Electricity data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

This dataset provides comprehensive energy consumption data collected from various buildings in Southern California, covering the period from January 2018 to January 2024. The data includes hourly electricity usage records for residential, commercial, and industrial buildings, along with environmental and operational metrics. The dataset is intended for researchers and practitioners interested in electricity consumption forecasting, energy management, sustainability, and the development of AI-based optimization models. The diverse features and real-world scenarios captured in the dataset make it suitable for time-series analysis, regression tasks, and classification problems.

The data was collected from over 100 facilities across Southern California, integrating information from smart meters, IoT sensors, building management systems, and regional utility companies. It covers different seasons, significant events (e.g., public holidays, extreme weather), and varying energy consumption patterns, allowing for robust analysis of electricity usage trends and energy-saving opportunities.

Dataset Features:

Timestamp (DateTime): The date and time when the data was recorded, sampled hourly from 2018 to 2024. Building Type (Categorical): Type of building (Residential, Commercial, or Industrial). Energy Consumption (kWh) (Float): Total electricity consumption recorded in kilowatt-hours. Temperature (°C) (Float): Outdoor temperature at the time of consumption. Humidity (%) (Float): Relative humidity levels during the time of energy consumption. Occupancy Rate (%) (Float): Estimated percentage of occupied spaces in the building. Lighting Consumption (kWh) (Float): Electricity usage specifically for lighting within the building. HVAC Consumption (kWh) (Float): Energy used by heating, ventilation, and air conditioning systems. Energy Price ($/kWh) (Float): Cost of electricity at the time of consumption. Carbon Emission Rate (g CO2/kWh) (Float): Associated carbon emissions per unit of energy consumption. Power Factor (Float): Ratio of the real power used to the apparent power in the building. Reactive Power (kVAR) (Float): The reactive component of the electrical power consumed. Wind Speed (m/s) (Float): Wind speed at the time of the energy recording. Solar Radiation (W/m²) (Float): Solar energy incident on the building's location. Rainfall (mm) (Float): Recorded rainfall during the hour. Thermal Comfort Index (Float): An index representing perceived indoor comfort based on temperature and humidity. Peak Demand Indicator (Binary): Indicates whether the energy demand was during peak hours (1) or off-peak hours (0). Energy Savings Potential (%) (Float): Predicted potential savings in energy consumption based on current usage patterns. Carbon Emission Reduction Category (Categorical): Level of reduction in carbon emissions (Low, Medium, High, Very High). Holiday Indicator (Binary): Indicates whether the date corresponds to a public holiday (1) or a regular day (0). Weekend Indicator (Binary): Indicates if the day is a weekend (1) or a weekday (0). Usage: This dataset can be used for:

Time-series analysis and forecasting: Predict future energy consumption and identify seasonal patterns. Energy optimization modeling: Develop AI-based models for energy savings and load management. Classification and regression tasks: Multilabel prediction of energy-related factors, such as peak demand and carbon emission reduction. Sustainability research: Evaluate the impact of energy consumption on carbon emissions and explore ways to optimize for environmental benefits. Target Audience: Energy researchers, data scientists, machine learning practitioners, environmental analysts, and anyone interested in energy optimization and sustainable resource management.

Indonesia Energy: Consumption: Industry & Construction: Electricity data was reported at 336,474.000 TJ in 2017. This records an increase from the previous number of 295,044.000 TJ for 2016. Indonesia Energy: Consumption: Industry & Construction: Electricity data is updated yearly, averaging 207,337.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 336,474.000 TJ in 2017 and a record low of 132,255.000 TJ in 2014. Indonesia Energy: Consumption: Industry & Construction: Electricity data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

Estimated industrial manufacturing agriculture construction and mining energy estimated by North American Industrial Classification System NAICS code county and fuel type for 2014. Additional disaggregation by end use e.g. machine drive process heating facility lighting is provided for manufacturing agriculture and mining industries. Estimation approach is described in detail in the data_foundation folder here https//github.com/NREL/Industry-Energy-Tool/.

This dataset examines energy efficiency in industrial operations, focusing on factors that influence energy consumption efficiency. It is ideal for building machine learning models to predict the Efficiency_Class ("High," "Medium," "Low") of industrial systems based on operational and environmental features. Predictors include Energy_Consumed (kWh), Production_Output (tons), Renewable_Energy_Usage (%), Equipment_Age (years), and Downtime_Hours (hours/month). Additionally, variables like Automation_Level, Operator_Training, and Industry_Type provide qualitative insights into efficiency determinants.

This dataset offers significant utility for industries aiming to optimize energy usage, evaluate operational efficiency, and reduce costs. Analysts can use it to study correlations between energy consumption and operational practices or identify key drivers of high efficiency. The dataset is also valuable for research on industrial sustainability and automation impacts.

Electricity consumption rate in the building sector per subscriber increased by **** percent in Saudi Arabia in 2021 compared to a decline of *** percent the year before.

The dataset includes two-year monitoring data from the energy consumption of and office building located in center of Italy. The building has HVAC system, heat pumps for space heating /cooling (overall 120-140 KW load) and lighting subsystems controlled individually and/or overall by BMS.

The building is a part of a small smart-grid which includes a PV plant (180KW). Energy production data are monitored and a two-year dataset is provided as well.

Italy Electricity Consumption: Industry: Construction data was reported at 1,386.000 kWh mn in 2017. This records an increase from the previous number of 1,353.000 kWh mn for 2016. Italy Electricity Consumption: Industry: Construction data is updated yearly, averaging 1,355.000 kWh mn from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 1,888.100 kWh mn in 2008 and a record low of 1,043.300 kWh mn in 1997. Italy Electricity Consumption: Industry: Construction data remains active status in CEIC and is reported by Terna. The data is categorized under Global Database’s Italy – Table IT.RB007: Electricity Consumption.

Indonesia Energy: Consumption: Industry & Construction: Iron and Steel Industry: Hard Coal data was reported at 2,184.000 TJ in 2017. This records a decrease from the previous number of 21,318.000 TJ for 2016. Indonesia Energy: Consumption: Industry & Construction: Iron and Steel Industry: Hard Coal data is updated yearly, averaging 59,645.000 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 111,201.000 TJ in 2012 and a record low of 2,184.000 TJ in 2017. Indonesia Energy: Consumption: Industry & Construction: Iron and Steel Industry: Hard Coal data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

Building Energy Simulation Consulting Service Market Outlook

According to our latest research, the global Building Energy Simulation Consulting Service market size reached USD 2.85 billion in 2024, with a robust compound annual growth rate (CAGR) of 11.2% projected from 2025 to 2033. By 2033, the market is expected to attain a value of USD 7.37 billion. This growth is primarily driven by increasing regulatory emphasis on sustainable construction, heightened awareness of energy efficiency, and the global push towards achieving carbon neutrality in the built environment.

One of the primary growth factors fueling the Building Energy Simulation Consulting Service market is the worldwide implementation of stringent energy codes and green building standards. Governments and regulatory bodies are mandating energy performance benchmarks for new and existing buildings, compelling stakeholders to adopt advanced simulation and consulting services. These regulations are particularly prevalent in developed economies such as North America and Europe, where energy consumption in buildings constitutes a significant portion of overall energy use. Furthermore, international frameworks such as LEED, BREEAM, and other green building certifications are increasingly being adopted, necessitating comprehensive energy modeling and simulation to ensure compliance and optimal building performance.

Technological advancements are another crucial driver for the market. The evolution of sophisticated simulation tools and software, integration of artificial intelligence and machine learning, and the growing use of Building Information Modeling (BIM) have revolutionized the way energy performance is analyzed. These innovations enable consultants to deliver more accurate, data-driven insights and predictive analytics, enhancing the value proposition for clients. The convergence of digital twin technology and real-time data analytics is further expanding the scope and effectiveness of energy simulation consulting, allowing for continuous optimization throughout a buildingÂ’s lifecycle. This technological progress is making energy simulation consulting services more accessible and appealing to a broader range of end-users, including small and medium enterprises.

Another significant growth factor is the rising focus on cost savings and operational efficiency among building owners and operators. As energy costs continue to rise globally, stakeholders are increasingly recognizing the long-term financial benefits of investing in energy-efficient building design and retrofits. Energy simulation consulting services provide actionable recommendations that lead to measurable reductions in energy consumption, utility expenses, and carbon emissions. Additionally, the integration of renewable energy sources such as solar and wind into building systems is creating new opportunities for simulation consultants to optimize energy flows and maximize return on investment. The demand for such integrated solutions is expected to accelerate as more organizations commit to sustainability targets and corporate social responsibility initiatives.

In recent years, the trend of Engineering Service Outsourcing has gained significant momentum across various industries, including the building energy simulation sector. Companies are increasingly turning to specialized engineering service providers to leverage their expertise and advanced technology solutions. This outsourcing model allows firms to access a broader pool of talent and cutting-edge tools without the need for substantial in-house investments. By partnering with external engineering service providers, businesses can focus on their core competencies while benefiting from the specialized skills and innovative approaches offered by these experts. The integration of engineering service outsourcing into the building energy simulation consulting landscape is expected to enhance the efficiency and effectiveness of simulation projects, driving further growth in the market.

Regionally, North America and Europe currently dominate the Building Energy Simulation Consulting Service market, accounting for over 60% of the global market share in 2024. These regions benefit from mature construction industries, well-established regulatory frameworks, and a high level of awareness regarding energy efficiency. However, the Asia Pacific

Overview and Intended Use Cases

These scenarios establish a range of futures for U.S. buildings sector energy use and CO₂ emissions to 2050 using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).

Scout benchmark scenario data are suitable for the following example use cases:

• setting high-level policy goals for the U.S. buildings sector to 2050 (e.g., X% building CO₂ emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);

• exploring the effects of key dynamics driving U.S. buildings sector energy and CO₂ emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);

• determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO₂ emissions reductions to 2050 under a given set of assumptions; and/or

• identifying the energy and emissions impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio.

Scenario Summary

A total of 8 scenarios explore the effects of changes across both the demand- and supply-side of building energy use on annual U.S. building energy use and CO₂ emissions from 2022–2050. Scenarios are organized into three groups representing low, moderate, and best-case potentials for building decarbonization, respectively. Individual scenarios are distinguished by four input dimensions:

• market-available technology performance range (EE): the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;

• load electrification (EL): the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment;

• early retrofits (R): the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and

• power grid (P): the annual average CO₂ emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2022–2050), resolved by grid region.

Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Summary_Execution” XLSX.

Results data are reported as an annual time series (2022–2050) at both a national and regional (EMM grid region) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.

What's New in This Version

This set of benchmark scenarios carries forward elements of past versions of this dataset (previously titled “Scout Core Measures Scenario Analysis” and summarized in this paper) while also streamlining the scenario design and reflecting updated policy ambitions regarding deployment of building efficiency, flexibility, and electrification as well as power grid evolution. Three scenarios in the current dataset map back to past scenarios:

• Scenario 2.1: EE1.P1 -> Scenario 6: HR 1T-2T-3T

• Scenario 2.2: EE1.ELe1a.P1 -> Scenario 7: HR 1T-2T-3T FS0

• Scenario 2.3: EE1.ELe1b.P1 -> Scenario 8: HR 1T-2T-3T FS20

The following scenario features are new in this dataset:

• Measures in the “best available” tier are deployed with load flexibility features that are based on a previous study of the U.S. building-grid resource. Past versions reflected only efficiency and electrification measures.

• The effects of progressively raising the market-available technology performance “floor” are explored by including reference case technologies in the measure competition and assuming codes/standards remove these technologies from the market-available mix beginning in a certain year. Past versions only explored the effects of a higher technology “ceiling”.

• Increasing ambitions for the top “Prospective” tier of measure performance are reflected. Past versions mapped much of this measure tier to the 2016 BTO MYPP.

• Electrification is explored via both endogenous and exogenous model settings, where the former is based on Scout’s economic measure competition models and the latter is based on fuel switching scenarios developed by Guidehouse for the BTO E3 Initiative. Past versions only explored endogenous electrification.

• Inefficient electrification is explored (past versions did not explore inefficient electrification). In such cases, consumers switch fossil-based heating and water heating equipment to a mix of electric resistance and heat pump technologies, with the mix determined by AEO 2021 Reference Case sales share data for these technologies.

• The effects of early retrofitting behavior are isolated by running all but one scenario without early retrofits. Past versions assumed a 1% early retrofit rate.

• More aggressive grid scenarios are explored using Brattle’s GridSIM model. Two scenarios are included—an 80% decarbonized grid by 2050 and 100% decarbonized grid by 2035. Past versions used the AEO 2018 “$25 carbon allowance fee” side case, which reached ~73% carbon-free electricity generation (including nuclear) by 2050.