About this Dataset

2024 Buildings Technology Baseline

The Buildings Technology Baseline is a curated and regularly updated dataset of current and projected performance, retail, and installed price data for all major building energy technologies needed to enable cost/benefit analyses. Building technology analyses require an up-to-date understanding of installation costs and cost-effectiveness of key building energy efficiency technologies.

The dataset was assembled by Guidehouse during fiscal year 2024. Data was gathered from the 2024 National Residential Efficiency Measures Database (NREMDB), the 2023 Energy Information Administration Updated Buildings Sector Appliance and Equipment Costs and Efficiencies, DOE Lighting Market Model, the 2023 RSMeans database, and the 2020 Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics, Lawrence Berkeley National Laboratory data, various literature, as well as new data from online retailers, stakeholder interviews, and contractor databases in 2023 and 2024. The dataset has been reviewed by subject matter experts at NREL and DOE. The 2024 dataset release is intended to be a starting point for interested users to provide feedback.

This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.
Organization: Department of Energy
Last updated: 2025-10-25T07:39:39.805319
Tags: analysis, baseline, building, buildings, commercial, data, energy, energy-analysis, financial, model, modeling, performance, power, price, processed-data, residential, retail, technology

Tables

Commercial Dataset

@usgov.doe_gov_2024_buildings_technology_baseline.commercial_dataset

123.01 kB
1,746 rows
59 columns

CREATE TABLE commercial_dataset (
  "technology_measure_id" BIGINT,
  "b_tb_year" BIGINT,
  "sector" VARCHAR,
  "end_use_category" VARCHAR,
  "component" VARCHAR,
  "technology_measure" VARCHAR,
  "fuel_type" VARCHAR,
  "display_name" VARCHAR,
  "regression_output_units" VARCHAR,
  "mapping_to_most_granular_nremdb_tech_measure_name" VARCHAR,
  "mapping_to_most_granular_eia_scout_tech_measure_name" VARCHAR,
  "regression_metric_1_coefficient_low" DOUBLE  -- Regression Metric 1 - Coefficient-Low,
  "regression_metric_1_coefficient_mid" DOUBLE  -- Regression Metric 1 - Coefficient-Mid,
  "regression_metric_1_coefficient_high" DOUBLE  -- Regression Metric 1 - Coefficient-High,
  "regression_metric_1_metric" VARCHAR  -- Regression Metric 1 - Metric,
  "regression_metric_1_unit" VARCHAR  -- Regression Metric 1 - Unit,
  "regression_metric_1_lower_bound" DOUBLE  -- Regression Metric 1 - Lower Bound,
  "regression_metric_1_typical" DOUBLE  -- Regression Metric 1 - Typical,
  "regression_metric_1_high" VARCHAR  -- Regression Metric 1 - High,
  "regression_metric_1_upper_bound" DOUBLE  -- Regression Metric 1 - Upper Bound,
  "regression_metric_2_coefficient_low" DOUBLE  -- Regression Metric 2 - Coefficient-Low,
  "regression_metric_2_coefficient_mid" DOUBLE  -- Regression Metric 2 - Coefficient-Mid,
  "regression_metric_2_coefficient_high" DOUBLE  -- Regression Metric 2 - Coefficient-High,
  "regression_metric_2_metric" VARCHAR  -- Regression Metric 2 - Metric,
  "regression_metric_2_unit" VARCHAR  -- Regression Metric 2 - Unit,
  "regression_metric_2_lower_bound" DOUBLE  -- Regression Metric 2 - Lower Bound,
  "regression_metric_2_typical" DOUBLE  -- Regression Metric 2 - Typical,
  "regression_metric_2_high" DOUBLE  -- Regression Metric 2 - High,
  "regression_metric_2_upper_bound" DOUBLE  -- Regression Metric 2 - Upper Bound,
  "regression_intercept_low" DOUBLE  -- Regression Intercept - Low,
  "regression_intercept_mid" DOUBLE  -- Regression Intercept - Mid,
  "regression_intercept_high" DOUBLE  -- Regression Intercept - High,
  "typical_unit_multiplier" DOUBLE,
  "typical_unit_multiplier_units_name" VARCHAR,
  "typical_unit_multiplier_units_abbreviation" VARCHAR,
  "typical_retail_price_2023_low" VARCHAR  -- Typical Retail Price ($2023) - Low,
  "typical_retail_price_2023_mid" VARCHAR  -- Typical Retail Price ($2023) - Mid,
  "typical_retail_price_2023_high" VARCHAR  -- Typical Retail Price ($2023) - High,
  "new_construction" DOUBLE,
  "retrofit" DOUBLE,
  "new_construction_1" DOUBLE,
  "retrofit_1" DOUBLE,
  "typical_new_construction_installed_cost_2023_low" VARCHAR  -- Typical New Construction Installed Cost ($2023) - Low,
  "typical_new_construction_installed_cost_2023_mid" VARCHAR  -- Typical New Construction Installed Cost ($2023) - Mid,
  "typical_new_construction_installed_cost_2023_high" VARCHAR  -- Typical New Construction Installed Cost ($2023) - High,
  "typical_retrofit_installed_cost_2023_low" VARCHAR  -- Typical Retrofit Installed Cost ($2023) - Low,
  "typical_retrofit_installed_cost_2023_mid" VARCHAR  -- Typical Retrofit Installed Cost ($2023) - Mid,
  "typical_retrofit_installed_cost_2023_high" VARCHAR  -- Typical Retrofit Installed Cost ($2023) - High,
  "year" BIGINT,
  "scenario" VARCHAR,
  "lifetime_years" DOUBLE  -- Lifetime (Years),
  "cost_variation_considerations" VARCHAR,
  "source_diversity" DOUBLE,
  "sample_size" DOUBLE,
  "r_squared_value" DOUBLE,
  "data_age" BIGINT,
  "notes" VARCHAR,
  "unnamed_57" VARCHAR  -- Unnamed: 57,
  "unnamed_58" VARCHAR  -- Unnamed: 58
);

Residential Dataset

@usgov.doe_gov_2024_buildings_technology_baseline.residential_dataset

143.28 kB
3,351 rows
57 columns

CREATE TABLE residential_dataset (
  "technology_id" DOUBLE,
  "b_tb_year" DOUBLE,
  "sector" VARCHAR,
  "end_use_category" VARCHAR,
  "component" VARCHAR,
  "technology_measure" VARCHAR,
  "fuel_type" VARCHAR,
  "display_name" VARCHAR,
  "regression_output_units" VARCHAR,
  "mapping_to_most_granular_nremdb_tech_measure_name" VARCHAR,
  "mapping_to_most_granular_eia_scout_tech_measure_name" VARCHAR,
  "regression_metric_1_coefficient_low" DOUBLE  -- Regression Metric 1 - Coefficient-Low,
  "regression_metric_1_coefficient_mid" DOUBLE  -- Regression Metric 1 - Coefficient-Mid,
  "regression_metric_1_coefficient_high" DOUBLE  -- Regression Metric 1 - Coefficient-High,
  "regression_metric_1_metric" VARCHAR  -- Regression Metric 1 - Metric,
  "regression_metric_1_unit" VARCHAR  -- Regression Metric 1 - Unit,
  "regression_metric_1_lower_bound" DOUBLE  -- Regression Metric 1 - Lower Bound,
  "regression_metric_1_typical" DOUBLE  -- Regression Metric 1 - Typical,
  "regression_metric_1_high" DOUBLE  -- Regression Metric 1 - High,
  "regression_metric_1_upper_bound" DOUBLE  -- Regression Metric 1 - Upper Bound,
  "regression_metric_2_coefficient_low" DOUBLE  -- Regression Metric 2 - Coefficient-Low,
  "regression_metric_2_coefficient_mid" DOUBLE  -- Regression Metric 2 - Coefficient-Mid,
  "regression_metric_2_coefficient_high" DOUBLE  -- Regression Metric 2 - Coefficient-High,
  "regression_metric_2_metric" VARCHAR  -- Regression Metric 2 - Metric,
  "regression_metric_2_unit" VARCHAR  -- Regression Metric 2 - Unit,
  "regression_metric_2_lower_bound" VARCHAR  -- Regression Metric 2 - Lower Bound,
  "regression_metric_2_typical" DOUBLE  -- Regression Metric 2 - Typical,
  "regression_metric_2_high" DOUBLE  -- Regression Metric 2 - High,
  "regression_metric_2_upper_bound" VARCHAR  -- Regression Metric 2 - Upper Bound,
  "regression_intercept_low" DOUBLE  -- Regression Intercept - Low,
  "regression_intercept_mid" DOUBLE  -- Regression Intercept - Mid,
  "regression_intercept_high" DOUBLE  -- Regression Intercept - High,
  "typical_unit_multiplier" DOUBLE,
  "typical_unit_multiplier_units_name" VARCHAR,
  "typical_unit_multiplier_units_abbreviation" VARCHAR,
  "typical_retail_price_2023_low" VARCHAR  -- Typical Retail Price ($2023) - Low,
  "typical_retail_price_2023_mid" VARCHAR  -- Typical Retail Price ($2023) - Mid,
  "typical_retail_price_2023_high" VARCHAR  -- Typical Retail Price ($2023) - High,
  "installation_multiplier_new_construction" DOUBLE  -- Installation Multiplier - New Construction,
  "installation_multiplier_retrofit" DOUBLE  -- Installation Multiplier - Retrofit,
  "installation_adder_new_construction" DOUBLE  -- Installation Adder - New Construction,
  "installation_adder_retrofit" DOUBLE  -- Installation Adder - Retrofit,
  "typical_new_construction_installed_cost_2023_low" VARCHAR  -- Typical New Construction Installed Cost ($2023) - Low,
  "typical_new_construction_installed_cost_2023_mid" VARCHAR  -- Typical New Construction Installed Cost ($2023) - Mid,
  "typical_new_construction_installed_cost_2023_high" VARCHAR  -- Typical New Construction Installed Cost ($2023) - High,
  "typical_retrofit_installed_cost_2023_low" VARCHAR  -- Typical Retrofit Installed Cost ($2023) - Low,
  "typical_retrofit_installed_cost_2023_mid" VARCHAR  -- Typical Retrofit Installed Cost ($2023) - Mid,
  "typical_retrofit_installed_cost_2023_high" VARCHAR  -- Typical Retrofit Installed Cost ($2023) - High,
  "projection_year" DOUBLE,
  "projection_scenario" VARCHAR,
  "lifetime_years" VARCHAR  -- Lifetime (Years),
  "cost_variation_considerations" VARCHAR,
  "source_diversity" VARCHAR,
  "sample_size" VARCHAR,
  "r_squared_value" DOUBLE,
  "data_age" DOUBLE,
  "notes" VARCHAR
);