Analysis of ‘Energy Efficiency Portfolio Standard (EEPS) Program Estimated Energy Savings Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/cf47985d-9e78-478d-b83a-4fcebff9b63a on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The Energy Efficiency Portfolio Standard (EEPS) Program encourages cost-effective electric and natural gas energy efficiency across New York State. New York State’s utilities and the New York State Energy Research and Development Authority (NYSERDA) administer energy efficiency programs to achieve energy efficiency savings. EEPS energy efficiency programs provide technical services, information and customer incentives to encourage customers in implementing energy efficiency measures. This data includes the list of energy efficiency programs and the estimated energy savings reported for each program.

--- Original source retains full ownership of the source dataset ---

This dataset was curated from an office building constructed in 2015 in Berkeley, California, which includes 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 (each 2,325 m2) 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. This dataset can be used for various types of applications, including building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls to improve understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.

Methods 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.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. 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. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases D I S C L A I M E R: Normalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter. Home Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings 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 Home Performance with ENERGY STAR 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. 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. This dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, 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 kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric. 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.

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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 experiencing significant growth due to increasing global awareness regarding environmental sustainability and the shift towards renewable energy sources. This trend is driving companies to invest in energy efficiency services to reduce their carbon footprint and save on operational costs. However, the high initial setup cost for implementing energy efficiency solutions remains a significant challenge for market growth. Key drivers of the market include stringent energy efficiency regulations, rising energy prices, and growing demand for sustainable business practices.
Furthermore, advancements in energy efficiency technologies, such as smart grid systems and energy storage solutions, are providing new opportunities for market expansion. Companies seeking to capitalize on these opportunities must navigate the challenges of high upfront costs and complex implementation processes. Strategic partnerships, government incentives, and technology innovation will be essential for companies to successfully penetrate this market and establish a competitive position.

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

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Machine learning and artificial intelligence are transforming the energy efficiency services market by optimizing energy recovery and predicting energy usage patterns. Energy training and LEED certification are becoming essential for businesses seeking to reduce their carbon footprint and save costs. Cloud-based platforms enable remote monitoring and reporting of energy efficiency metrics, while energy rebates and grants incentivize the adoption of high-efficiency technologies such as heat recovery, smart sensors, and LED lighting. The Internet of Things and energy performance contracts are driving the integration of variable speed drives, building envelope upgrades, and energy modeling into energy efficiency solutions.
Simulation software and predictive maintenance are also gaining traction, allowing businesses to identify energy savings opportunities and optimize their energy usage. Energy efficiency trends include the increasing use of waste heat recovery, energy efficiency reporting, and energy efficiency innovations such as high-efficiency motors and energy service companies. Energy efficiency awareness is growing, with businesses recognizing the importance of reducing energy consumption and improving operational efficiency. The future of energy efficiency lies in the integration of these technologies and the continued innovation in energy efficiency solutions.

How is this Industrial Energy Efficiency Services Industry segmented?

The industrial energy efficiency services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  EA and C
  Monitoring and verification
  Product and system optimization


End-user

  Oil and gas
  Power
  Petrochemicals and chemicals
  Others


Deployment

  Cloud-based
  On-premises


Revenue Stream

  Energy-as-a-service (EaaS)
  Performance-based contracts
  Subscription-based services
  One-time consultation and implementation


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Service Insights

The ea and c segment is estimated to witness significant growth during the forecast period.

The market encompasses process optimization, energy efficiency technologies, and consulting services. In the EA and C segment, there is a substantial market share. Energy management contractors closely monitor and analyze data from major energy-consuming devices, such as lighting, steam systems, fired heaters, refrigeration and air-conditioning systems, and electric motor-driven systems in industrial settings. This data is utilized to devise strategies aimed at reducing overall energy consumption within manufacturing facilities. Equipment upgrades, energy efficiency assessments, and HVAC optimization are essential components of industrial facility upgrades. Energy efficiency software is employed to record and analyze data, employing a range of calculators to estimate potential energy savings.

Consulting services are indispensable during the inception of industrial projects, offering expertise in energy efficiency policies, regulations, and incentives. Sustainability initiatives, including renewable energy integration and carbon

🏡 Household Energy Consumption - April 2025 (90,000 Records)

📌 Overview

This dataset presents detailed energy consumption records from various households over the month. With 90,000 rows and multiple features such as temperature, household size, air conditioning usage, and peak hour consumption, this dataset is perfect for performing time-series analysis, machine learning, and sustainability research.

📂 Dataset Summary

• Rows: 90,000
• Time Range: April 1, 2025 – April 30, 2025
• Data Granularity: Daily per household
• Location: Simulated global coverage
• Format: CSV (Comma-Separated Values)

📚 Libraries Used for Working with household_energy_consumption_2025.csv

🔍 1. Data Manipulation & Analysis

📊 2. Data Visualization

📈 3. Machine Learning / Modeling

🧹 4. Data Preprocessing

🧪 5. Model Evaluation

✅ These libraries provide a complete toolkit for performing data analysis, modeling, and visualization tasks efficiently.

📈 Potential Use Cases

This dataset is ideal for a wide variety of analytics and machine learning projects:

🔮 Forecasting & Time Series Analysis

• Predict future household energy consumption based on previous trends and weather conditions.
• Identify seasonal and daily consumption patterns.

💡 Energy Efficiency Analysis

• Analyze differences in energy consumption between households with and without air conditioning.
• Compare energy usage efficiency across varying household sizes.

🌡️ Climate Impact Studies

• Investigate how temperature affects electricity usage across households.
• Model the potential impact of climate change on residential energy demand.

🔌 Peak Load Management

• Build models to predict and manage energy demand during peak hours.
• Support research on smart grid technologies and dynamic pricing.

🧠 Machine Learning Projects

• Supervised learning (regression/classification) to predict energy consumption.
• Clustering households by usage patterns for targeted energy programs.
• Anomaly detection in energy usage for fault detection.

🛠️ Example Starter Projects

• Time-series forecasting using Facebook Prophet or ARIMA
• Regression modeling using XGBoost or LightGBM
• Classification of AC vs. non-AC household behavior
• Energy-saving recommendation systems
• Heatmaps of temperature vs. energy usage

Analysis of ‘Energy Efficiency Annual Energy Savings ( MWH)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c2ca9eac-10d3-46c5-8459-11e1fb2a9539 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Austin Energy provides rebates and low interest loans to customers who make energy efficiency improvements. During fiscal year 2012, energy savings totaled nearly 106-million kilowatt hours. That's enough electricity to power nearly 9,400 residential homes in Austin.

--- Original source retains full ownership of the source dataset ---

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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For more datasets, click here.

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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...

Analysis of ‘Energy Efficiency Energy Savings (MWH)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4cb23de0-eed2-4164-b776-de071a8f5361 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The utility has a diverse repertoire of efficiency programs, including Austin Energy Green Building, PowerSaver and Demand Response, that help customers reduce energy and save money. These efforts directly benefit customers as well as set the utility on the path to meet goals put in place by Austin City Council. Find more information at http://austinenergy.com/go/advantage and http://austinenergy.com/go/reports.

--- Original source retains full ownership of the source dataset ---

The Energy Data Analytics market is experiencing robust growth, driven by the increasing need for efficient energy management and optimization across various sectors. The market's expansion is fueled by several key factors. Firstly, the global shift towards renewable energy sources necessitates advanced analytics capabilities to predict, manage, and optimize renewable energy generation and distribution. Secondly, the imperative to reduce carbon emissions and improve energy efficiency is pushing companies and governments to adopt sophisticated data analytics solutions for monitoring and controlling energy consumption. This includes the use of predictive modeling to identify potential energy waste and optimize operational efficiency. Thirdly, advancements in technologies like artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of energy data analytics platforms, providing more accurate predictions and actionable insights. This is leading to broader adoption across upstream, midstream, and downstream segments of the energy industry. The market is segmented by application (Enterprise, Government) and service type (Upstream Exploration Services, Quality Testing Services, Midstream and Downstream Services), reflecting the diverse needs across the energy value chain. Key players like Virtusa Corp, DNV, and Siemens are actively shaping the market landscape through innovative solutions and strategic partnerships. While the market faces challenges like data security concerns and the need for skilled professionals, the overall growth trajectory remains positive. The geographical distribution of the market reflects the varying levels of technological adoption and regulatory frameworks across different regions. North America, with its advanced technological infrastructure and stringent environmental regulations, currently holds a significant market share. However, regions like Asia-Pacific, driven by rapid industrialization and growing energy demands, are experiencing significant growth and are expected to witness substantial expansion in the coming years. Europe's established energy market and focus on sustainability will continue to drive demand for sophisticated analytics solutions. The projected Compound Annual Growth Rate (CAGR) suggests a sustained period of expansion, indicating a promising outlook for investors and market participants. The market's future will depend on ongoing technological advancements, regulatory support for sustainable energy practices, and the increasing awareness among businesses and governments about the benefits of data-driven energy management.

The Big Data Analytics in Energy market is experiencing robust growth, driven by the increasing need for efficient grid operations, smart metering deployments, and advanced asset & workforce management. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. This expansion is fueled by the ever-increasing volume of data generated by smart grids, renewable energy sources, and energy consumption patterns. Utilities are leveraging big data analytics to optimize energy distribution, enhance grid reliability, reduce operational costs, and improve customer service. The cloud-based segment is expected to dominate the market due to its scalability, flexibility, and cost-effectiveness compared to on-premise solutions. North America and Europe currently hold significant market share, driven by early adoption of smart grid technologies and supportive government regulations. However, Asia Pacific is poised for substantial growth in the coming years, fueled by rapid urbanization, increasing energy demand, and government initiatives promoting smart city development. Competitive pressures among major players like IBM, Microsoft, SAP, and Accenture are driving innovation and fostering market expansion. The adoption of advanced analytics techniques, including machine learning and artificial intelligence, is revolutionizing energy management. Predictive maintenance using big data analytics helps prevent equipment failures, minimizing downtime and reducing maintenance costs. Smart metering data analysis allows for improved energy efficiency, demand-side management, and better customer engagement. The integration of big data analytics with IoT devices is further enhancing operational visibility and facilitating real-time decision-making. Despite the positive outlook, challenges remain, including data security concerns, the need for skilled professionals, and the complexity of integrating legacy systems with modern big data platforms. However, these challenges are not insurmountable, and ongoing technological advancements and increasing industry collaboration are expected to propel market growth.

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.

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.

Market Size and Growth: The global Energy Efficiency as a Service (EEaaS) market size is projected to reach USD 63.8 billion by 2033, expanding at a CAGR of 23.0% during the forecast period 2023-2033. Rising energy costs, increasing awareness of environmental sustainability, and government incentives for energy conservation are driving the market's growth. Moreover, the increasing adoption of digital technologies and the integration of IoT devices are further fueling market expansion. Key Market Trends and Players: Major trends in the EEaaS market include the growing emphasis on data analytics for energy optimization, the adoption of smart sensors and building management systems, and the emergence of performance-based contracts. Key players in the market include Enel Spa, ENGIE SA, General Electric, Honeywell International, Johnson Controls International, Schneider Electric SE, SGS SA, Siemens AG, VEOLIA ENVIRONNEMENT, and TÜV SÜD. These companies offer a wide range of EEaaS solutions, including energy audits, equipment upgrades, financing, and performance guarantees.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income. 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 54 percent of the Estimated Annual kWh Savings and 70 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. 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. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf. This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD). 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.

Analysis of ‘Home Performance Energy Efficiency Projects in Maryland’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0bfa0bab-d02d-43eb-9410-1052198aedf0 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Through grants and loans, the Maryland Energy Administration has contributed to the growth of energy efficiency industries, and has helped reduce statewide energy consumption.

--- Original source retains full ownership of the source dataset ---

The data of city-and-county-level energy consumption and energy efficiency provided in this study are valuable with practical applications in the field of energy economics, management, and policy, including the following: First, the provided data have the characteristics of wide spatial coverage and long time span. This unique panel-structured dataset can be used to observe the trajectories and spatial differences of energy consumption and energy efficiency on a micro-level than at national and provincial levels. Therefore, it can also be used to analyze the factors driving the changes and spatial differences of energy consumption and energy efficiency in cities and counties. Second, the panel-structured dataset of energy consumption and energy efficiency can be used to match other economic data at the city and county levels, and studies such as the economic effects of energy consumption, the environmental and social effects of energy consumption, and the coupling relationship between energy efficiency and economic development may be conducted. Third, the development of energy consumption and energy efficiency data at the city and county levels can not only contribute to the energy management at China’s grassroot-level governments, such as in the formulation and implementation of road maps for energy transformation and energy efficiency improvement, but also provide a basis for the central and provincial governments to allocate the energy rights of cities and counties under the constraints of “carbon peak” and “carbon-neutral” targets. Fourth, the development of energy consumption and energy efficiency data at city and county levels could provide a more accurate assessment of the impact of the central government’s energy saving, emission reduction, and low-carbon green policies, as well as other socioeconomic policies, for example, assessment of the impact of the “central heating,” “coal to electricity,” low-carbon pilot city, and carbon emission trading right pilot policies on energy consumption and energy efficiency. Fifth, the method of retrieving micro-level energy consumption data by using satellite night-light data can also provide a reference for other developing countries and regions with limited energy statistics to evaluate their energy consumption and energy efficiency at the sub-national level.

Analysis of ‘Energy Efficiency Program Participation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7f2c3315-8b98-4b02-a54c-6f0089b09638 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Between 1982 and 2006, Austin Energy's energy efficiency programs offset the need to build a 700 megawatt (MW) power plant. This became known as Austin Energy's first conservation power plant. In 2007, Austin Energy kicked off a new goal with the Austin Climate Protection Plan, which is to offset another 800 MW of peak energy demand by 2020. Between 2007 and 2012, Austin Energy has offset an additional 318 MW which is 40% of the 800 MW goal.

Note: Total participation does not include GB commercial square foot.

Blank cells indicate data are not available because the program either had not started, has been discontinued, or we no longer track that data.

--- Original source retains full ownership of the source dataset ---