This Data Set is collected from UCI Machine Learning Repository.

Data Set Description in UCI as follows: " Abstract: This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.

We perform energy analysis using 12 different building shapes simulated in Ecotect. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer. "

Residential Home Energy Efficiency

Evaluated Meter Project Data 2007-2012

By State of New York [source]

About this dataset

This dataset provides energy efficiency meter evaluated data from 2007-2012 for residential existing homes (one to four units) in New York State. It includes the following data points: Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units .Year Home Built , Total Project Cost , Contractor Incentive , Total Incentives , Amount Financed through Program , Estimated Annual Electric Savings (kWh), Estimated Annual Gas Savings (MMBtu), Estimated First Year Energy Bill Savings ($) Baseline Electric (kWh), Baseline Gas (MMBtu), Reporting Electric (kWh), Reporting Gas (MMBtu ),Evaluated Annual Electric Savings( kWh ), Evaluated Annual gas Savings( MMBTU )Central Hudson LIPA National Fuel gas NYSEG Orange and Rockland Rochester Gas and electric Location 1. This dataset backcasts estimated modeled savings for a subset of 2007 -2012 completed projects in the Home Performance with ENERGY STARprogram against normalized savings calculated by an open source energy efficiency meter. The open source code uses utility grade metered consumption to weather normalize the pre -and post consumption data using standard methods with no discretionary independent variables. It is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector and help inform stakeholders interested in Pay For Performance programs where providers are paid for realizing measurable weather normalized results. Please make sure you read the Disclaimer included before using this data; it contains important information about evaluating savings from contractor reported modeling estimates as well as evaluating Normalized Savigns using Open Source OEE meter

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How to use the dataset

• Last updated information: The last update for this dataset was 2019-11-15

• Data Elements Overview:This dataset includes a variety of data points that provide valuable insights into residential energy efficiency projects undertaken between 2007 TO 2012 in New York State; including project ID, county, city zip code, climate zone, weather station used for normalization methods, completion date customer type size and volume of home number of units year home was built total project cost contractor incentive total incentives amount financed through program etc.

• Definitions Overview: There are several acronyms included in this datasets such as Central Hudson (a utility company), LIPA (the Long Island Power Authority), National Fuel Gas (National Fuel Gas Utility Company), NYSEG (New York State Electric & Gas Utility Company) and Rochester Gas & Electric (Rochester Gas & Electric Utility Company). Additionally “Climate Zone” are numbered 1 through 5 representing regions from coolest north/northwest regions to warmest south/southeast regions across New York; these correspond with Warm-Humid, Marine VBZc&De2VBladium Marine Subtropical HotSummer ColdWinter ColdSummer Moderate Winter regions respectively. A Weather Station is used for normalizing Savings Data which a location like described Niagara Falls International Airport that obtains historical average temperature values from various temperatures sources . Weather Stations Normalization compares day-of vs seasonal temperature difference outside homes against model prediction retrofit reduction predictions inside home without weather normalizing watt reduction products can be over or under estimated depending on current season vs expected seasons which this model accounts The estimated annual electric savings are calculated using factors such as pre-retrofit baseline electric kWh post-retrofit usage electric kWh evaluated annual electric savings calculated by open source library software installed by customers neighborhood ? measured GHG emission reductions determined with assumptions provided input device SDK so on life cycle greenhouse gas emission reductions also tracked documented impact studies have been conducted verify conclusion accuracy projected values reported nyserda industry rebate programs benchmarking standardized meter data allowing future compare patterns? measurements document capture utilities grid management initiated demand response events companies target focus market . Moving forward Total Project Cost is figure analyzed depending estimates provided

Research Ideas

• Developing an in...

This dataset includes data of whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, and occupant counts. The data was collected in three years from more than 300 sensors and meters for two office floors of the building. A three-step data curation strategy is applied to transform the raw data into the research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; (3) describing the metadata of the dataset using a semantic JSON schema.

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...

Discover the booming Data Center Energy Saving Solutions market, projected to reach $45 billion by 2033 with a 12% CAGR. Explore key drivers, trends, restraints, and regional insights for this rapidly expanding sector, dominated by leading players like Johnson Controls and Schneider Electric. Learn about innovative solutions in refrigeration, power supply, and AI applications driving efficiency and sustainability.

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Industrial Energy Efficiency Services Market Size 2025-2029

The industrial energy efficiency services market size is forecast to increase by USD 3.82 billion, at a CAGR of 5.1% between 2024 and 2029.

The market is a dynamic and evolving landscape, driven by the increasing emphasis on reducing energy consumption and minimizing environmental impact. Companies across various sectors are recognizing the importance of energy efficiency in their operations, leading to a growing demand for specialized services. One notable trend in the market is the integration of advanced technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), to optimize energy usage and improve overall efficiency. These technologies enable real-time monitoring and analysis of energy consumption patterns, allowing businesses to identify areas for improvement and implement targeted solutions. Another significant factor influencing the market is the rising cost of energy.
As energy prices continue to climb, companies are increasingly motivated to invest in energy efficiency measures to reduce their overall energy costs and improve their bottom line. However, the high initial setup costs associated with implementing these technologies can be a barrier to entry for some organizations. Despite these challenges, the market for industrial energy efficiency services is expected to continue growing as businesses seek to reduce their carbon footprint and improve their sustainability credentials. According to recent studies, the market is projected to grow at a steady pace, with energy efficiency services accounting for a significant share of the total energy management market.
For instance, a recent analysis revealed that energy efficiency services represented approximately 20% of the total energy management market in 2020, with a projected growth rate of around 12% between 2021 and 2026. This trend is expected to continue as more businesses prioritize energy efficiency and sustainability in their operations. In conclusion, the market is a dynamic and evolving landscape, driven by the growing awareness of environmental impact and the rising cost of energy. The integration of advanced technologies and the increasing demand for energy efficiency solutions are key factors fueling the market's growth. Despite the high initial setup costs, the long-term cost savings and sustainability benefits make energy efficiency a worthwhile investment for businesses across various sectors.

Major Market Trends & Insights

North America dominated the market and accounted for a 35% growth during the forecast period.
The market is expected to grow significantly in Europe as well over the forecast period.
By the Service, the EA and C sub-segment was valued at USD 5.27 billion in 2023
By the End-user, the Oil and gas sub-segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 45.70 million
Future Opportunities: USD 3.817 billion 
CAGR : 5.1%
North America: Largest market in 2023

What will be the Size of the Industrial Energy Efficiency Services Market during the forecast period?

Gain strategic insights into adoption of energy-efficient technologies Request Free Sample

Industrial energy efficiency services represent a significant market segment, with current adoption estimated at approximately 25% of total industrial energy consumption. This figure underscores the potential for substantial savings and environmental benefits.
The global industrial energy efficiency services market is evolving as businesses prioritize sustainability, cost optimization, and regulatory compliance. Energy audits, real-time monitoring systems, and advanced automation solutions are becoming essential for reducing energy waste and improving operational efficiency. Predictive maintenance, smart sensors, and data analytics platforms enable industries to track energy usage and implement corrective actions, leading to measurable performance gains. Industry data shows that energy optimization services contributed to a 15% reduction in energy costs for major industrial operations, with the market projected to expand by 14% during the forecast period, supported by growing adoption of green manufacturing practices. Digitalization, IoT-enabled monitoring, and cloud-based control systems are further transforming traditional energy management into proactive, AI-driven strategies. These trends create significant opportunities for service providers focusing on performance contracting, system integration, and customized efficiency solutions that address both cost and environmental goals. Looking ahead, market growth is projected to reach 5% annually, driven by increasing awareness of operational cost reduction and sustainability initiatives. Notably, energy efficiency measures such as HVAC maintenance, equipment upgrades, and energy monitor

The Buildings Performance Database (BPD) unlocks the power of building energy performance data. The platform enables users to perform statistical analysis on an anonymous dataset of tens of thousands of commercial and residential buildings from across the country. Users can compare performance trends among similar buildings to identify and prioritize cost-saving energy efficiency improvements and assess the range of likely savings from these improvements. Key Features - The BPD contains actual data on tens of thousands of existing buildings--not modeled data or anecdotal evidence. The BPD enables statistical analysis without revealing information about individual buildings. The BPD cleanses and validates data from many sources and translates it into a standard format. Analysis Tools - Peer Group Tool. Allows users to peruse the BPD and create peer groups based on specific building types, locations, sizes, ages, equipment and operational characteristics. Users can compare the energy use of their own building to a peer group of BPD buildings. Retrofit Analysis Tool. Allows users to analyze the savings potential of specific energy efficiency measures. Users can compare buildings that utilize one technology against peer buildings that utilize another. Coming Soon! Data Table Tool. Allows users to generate and export statistical data about peer groups. Financial Forecasting Tool. Forecasts cash flows for energy efficiency projects. Application Programming Interface (API). Allows external software to conduct analysis of the BPD data.

The Energy Data Analytics market is booming, projected to reach $35 billion by 2033 with a 12% CAGR. Discover key trends, drivers, and challenges shaping this dynamic sector, including the impact of AI, renewable energy, and smart grids. Learn about leading companies and regional market shares.

Snapshot img

AI Energy Efficiency Tools Market Size 2025-2029

The ai energy efficiency tools market size is valued to increase by USD 23.5 billion, at a CAGR of 34.7% from 2024 to 2029. Escalating energy costs and heightened price volatility will drive the ai energy efficiency tools market.

Market Insights

North America dominated the market and accounted for a 39% growth during the 2025-2029.
By Component - Software segment was valued at USD 1.44 billion in 2023
By Deployment - Cloud-based segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million 
Market Future Opportunities 2024: USD 23500.20 million
CAGR from 2024 to 2029 : 34.7%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to mitigate escalating energy costs and heightened price volatility. Advanced AI technologies, such as machine learning and deep learning, are being employed to optimize energy usage in real-time, enabling organizations to reduce their carbon footprint and lower operational expenses. One notable trend in this space is the emergence of AI-powered digital twins, which create virtual replicas of physical assets to optimize their performance and identify energy savings opportunities. However, the integration of diverse data sources and the implementation of these advanced technologies come with their own challenges. Data integration complexity and cybersecurity risks necessitate robust security frameworks and interoperability standards to ensure secure and seamless data exchange. In a supply chain optimization scenario, AI energy efficiency tools can analyze real-time data from various sources to optimize energy usage across the entire supply chain, reducing costs and improving overall efficiency. Despite these challenges, the potential benefits of AI energy efficiency tools make them an indispensable investment for businesses aiming to stay competitive in today's energy landscape.

What will be the size of the AI Energy Efficiency Tools Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, offering innovative solutions for sustainable energy management. Predictive analytics tools play a pivotal role in optimizing energy consumption patterns and identifying opportunities for energy savings. Renewable energy sources, such as solar and wind, are increasingly integrated into energy portfolios, necessitating advanced metering infrastructure and energy auditing software for effective management. Energy infrastructure upgrades, driven by energy sector decarbonization initiatives, require data-driven decision making. Smart building technology, including IoT energy monitoring and energy management software, enables real-time energy usage analysis and cost optimization strategies. Thermal imaging technology and process optimization AI facilitate energy conservation measures and grid stability improvements. Moreover, energy contract optimization and green building certification help companies meet emission reduction targets and comply with building codes. Energy data visualization tools provide valuable insights into energy system resilience and power quality analysis. In the face of the low-carbon energy transition, businesses must adopt these technologies to remain competitive and contribute to a more sustainable energy future.

Unpacking the AI Energy Efficiency Tools Market Landscape

In today's business landscape, the implementation of AI energy efficiency tools has become a strategic priority for organizations seeking to optimize energy usage and reduce costs. According to industry data, AI-powered battery management systems have led to a 15% increase in energy efficiency, resulting in significant cost savings for businesses. Furthermore, machine learning models in cloud-based energy platforms enable peak demand reduction by up to 20%, aligning with compliance requirements and improving ROI. HVAC optimization techniques, fueled by natural language processing and deep learning applications, have shown a 12% reduction in energy consumption. These tools also facilitate demand-side management, sensor data analytics, and energy storage management, contributing to carbon footprint reduction and smart grid optimization. Additionally, AI-powered energy control in data centers, wind energy optimization, and solar energy forecasting have proven effective in enhancing energy performance indicators. Overall, the adoption of AI energy efficiency tools delivers tangible business outcomes, including cost savings, improved ROI, and regulatory compliance.

Key Market Drivers Fueling Growth

The escalating energy costs and heightened price volatility serve as the primary drivers in the market, necessitating close attention from professionals and investors alike. The mar

Discover the booming Energy Data Analytics market! This comprehensive analysis reveals key drivers, trends, and restraints shaping the $15 billion (2025 est.) industry, covering segments, key players, and regional growth projections through 2033. Learn about the impact of AI, renewable energy, and carbon reduction initiatives.

The Building Performance Database (BPD) is the largest publicly-available source of measured energy performance data for buildings in the United States. It contains information about the building's energy use, location, and physical and operational characteristics. The BPD can be used by building owners, operators, architects and engineers to compare a building's energy performance against customized peer groups, identify energy performance opportunities, and set energy performance. It can also be used by energy performance program implementers to analyze energy performance features and trends in the building stock. The BPD compiles data from various data sources, converts it into a standard format, cleanses and quality checks the data, and provides users with access to the data in a way that maintains anonymity for data providers.

The BPD consists of the database itself, a graphical user interface allowing exploration of the data, and an application programming interface allowing the development of third-party applications using the data.

About the Project Increasing energy productivity holds some of the greatest possibilities for enhancing the welfare countries get out of their energy systems. It also recasts energy efficiency in terms of boosting competitiveness and wealth, more powerfully conveying its profound benefits to society. KAPSARC and UNESCWA have initiated this project to explore the energy productivity potential of the Arab region, starting with the six GCC countries and later extending to other countries. Aimed at policymakers, this project highlights the social gains from energy productivity investments, where countries are currently at, and pathways to achieving improved performance in this area. Key Points More than 75 percent of the total electricity consumed in Oman is attributed to buildings, with almost 50 percent used by households. The absence of mandatory energy efficiency regulations for buildings, coupled with population growth, has led to a significant increase in annual energy consumption and peak power demand in the country – both averaging growth rates of 10 percent over the last five years. We used an energy productivity analysis approach to analyze the benefits of large-scale energy efficiency programs in new and existing buildings. Our study finds: Investment in energy efficiency measures to retrofit existing buildings could lead to significant economic and environmental benefits. The potential for energy savings will vary depending on implementation costs and scale of retrofits. The benefits that can be realized for residential buildings are significantly higher than those obtained for commercial or governmental buildings. If a minimal Level-1 energy retrofit program is applied to existing residential buildings, savings of 957 GWh/year in electricity consumption and 214 MW in peak power demand can be achieved. Moreover, if a Level-3 deep retrofit of energy efficiency measures is implemented for the residential sector, savings soar to 6,000 GWh/year in electricity consumption and 1,300 MW in peak power demand. Also, 4 million metric tonnes per year of carbon emissions will be eliminated. A Level-3 retrofit of the entire building stock in Oman can result in savings of 10,000 GWh/year in electricity consumption and 2,300 MW in peak power demand. Additionally, there would be a 7 million metric tonnes per year of reduction in carbon emissions The economic impact of the buildings' energy efficiency retrofit program is the potential to create new employment in Oman. The direct effects for retrofitting buildings include jobs needed to implement energy efficiency measures while the indirect effects are associated with work needed to produce and supply energy efficiency equipment and materials.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. The Residential Existing Homes Program is a market transformation program that uses Building Performance Institute (BPI) Goldstar contractors to install comprehensive energy-efficient improvements. The program is designed to use building science and a whole-house approach to reduce energy use in the State’s existing one-to-four family and low-rise multifamily residential buildings and capture heating fuel and electricity-related savings. The Program provides income-based incentives, including an assisted subsidy for households with income up to 80% of the State or Median County Income, whichever is higher to install eligible energy efficiency improvements including building shell measures, high efficiency heating and cooling measures, ENERGY STAR appliances and lighting. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Many factors influence the degree to which estimated savings are realized, including proper calibration of the savings model and the savings algorithms used in the modeling software. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. Beginning November 2017, the Program requires the use of HPXML-compliant modeling software tools and data quality protocols have been implemented to more accurately project savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. The New York Residential Existing Homes (One to Four Units) dataset includes the following data points for projects completed during Green Jobs Green-NY, beginning November 15, 2010: Home Performance Project ID, Home Performance Site ID, Project County, Project City, Project Zip, Gas Utility, Electric Utility, Project Completion Date, Customer Type, Low-Rise or Home Performance Indicator, Total Project Cost (USD), Total Incentives (USD), Type of Program Financing, Amount Financed Through Program (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Volume of Home, Number of Units, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Energy Savings $ Estimate (USD), Homeowner Received Green Jobs-Green NY Free/Reduced Cost Audit (Y/N). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

The Energy Data Analytics market is booming, projected to reach $45 billion by 2033 at a CAGR of 12%. This in-depth analysis explores market drivers, trends, restraints, and key players, offering insights into the lucrative opportunities in upstream, midstream, and downstream energy sectors. Discover the future of energy analytics.

Energy Efficiency Analytics Market Outlook

According to our latest research, the global energy efficiency analytics market size reached USD 8.4 billion in 2024, driven by rapid advancements in digital technologies and increasing demand for sustainable energy solutions across industries. The market is expected to expand at a robust CAGR of 15.2% from 2025 to 2033, ultimately reaching a forecasted value of USD 28.4 billion by 2033. This strong growth trajectory is fueled by the widespread adoption of analytics platforms to optimize energy consumption, reduce operational costs, and comply with evolving regulatory mandates for energy efficiency.

One of the primary growth factors for the energy efficiency analytics market is the intensifying global focus on sustainability and carbon footprint reduction. As governments and industries worldwide commit to ambitious net-zero targets and stricter energy efficiency regulations, organizations are increasingly turning to advanced analytics solutions to monitor, manage, and optimize their energy usage. The proliferation of Internet of Things (IoT) devices, smart meters, and connected sensors has generated vast amounts of real-time energy consumption data, providing fertile ground for analytics platforms to deliver actionable insights. This digital transformation is empowering businesses to identify inefficiencies, forecast energy demand, and implement strategic interventions, thereby driving the market forward.

Another significant driver is the growing integration of artificial intelligence (AI) and machine learning (ML) technologies within energy efficiency analytics platforms. These advanced technologies enable predictive analytics, anomaly detection, and automated optimization of energy systems. For example, AI-powered analytics can dynamically adjust heating, ventilation, and air conditioning (HVAC) systems in commercial buildings based on occupancy patterns, weather forecasts, and real-time energy pricing. In the industrial sector, ML algorithms can identify energy-intensive processes and suggest optimization measures to reduce waste and enhance productivity. The increasing sophistication and accessibility of these technologies are making energy analytics solutions more attractive and cost-effective for a broad spectrum of end-users.

The surge in smart infrastructure projects and urbanization, particularly in emerging economies, is also contributing to the market’s expansion. Governments and municipalities are investing heavily in smart city initiatives that prioritize energy efficiency and sustainability. These projects leverage energy analytics to optimize public lighting, transportation systems, and utility management, resulting in significant cost savings and environmental benefits. Furthermore, the growing trend of remote work and digitalization in the post-pandemic era has heightened the need for energy-efficient buildings and homes, further boosting demand for analytics solutions. As these trends continue to accelerate, the energy efficiency analytics market is poised for sustained growth across diverse applications and industries.

From a regional perspective, North America continues to lead the global energy efficiency analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technology vendors, early adoption of smart energy solutions, and stringent regulatory frameworks have positioned North America at the forefront of market development. Europe’s market is bolstered by ambitious climate goals and substantial investments in renewable energy and smart grid infrastructure. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid urbanization, industrial expansion, and government-led initiatives to improve energy efficiency. Latin America and the Middle East & Africa are also exhibiting steady progress, albeit from a smaller base, as they embrace digital transformation in their energy sectors.

Component Analysis

The energy efficiency analytics market by component is primarily segmented into software and services, each playing a pivotal role in enabling organizations to achieve optimal energy performance. The software segment encompasses platforms and applications that collect, process, and analyze energy data from various sources, delivering actionable insights through user-friendly dashboards and reports. These solutions are designed to integrate seamlessly with existing building management systems, industrial control systems, an

The size of the Energy Efficiency Management Platform market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.

The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from EIA's Annual Energy Outlook (AEO) Reference Case projections across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:

'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from AEO Summary Table A2.

'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.

'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each American Institute of Architects (AIA) climate zone. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.

'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).

'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.