GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Cold-Weather Heat Pump Model
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: KNN regression to predict hourly unitized impact of an upgraded CWHP in a residential home under historical and forecasted weather conditions.
12_outputs: Hourly load shape profile for heat pump
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/19/2023
19_date_acq_dev_began: 7/26/2023
20_date_implemented: 9/7/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: TVA TRM, ELRF, TMY Weather
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: EE/DR Smart Thermostat Impact Model
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: DR dispatch simulation to demonstrate hourly unitized impact of an EE/DR smart thermostat in a residential home under historical and forecasted weather & load conditions.
12_outputs: Hourly load shape profile for EEDR
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/7/2023
19_date_acq_dev_began: 6/7/2023
20_date_implemented: 9/29/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: TVA TRM, TVA Smart Thermostat Pilot data, ELRF, TMY Weather
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Standard EE/BE Measure Impact Models
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: Set of ~200 linear models for other EE & BE measures  to predict hourly unitized impactunder historical and forecasted weather conditions. Based on a standardized set of calendar and weather features.
12_outputs: Hourly load shape profiles for EE/BE
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2019
19_date_acq_dev_began: 8/1/2019
20_date_implemented: 10/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: TVA TRM, ELRF
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: EnergyRightPSPImpactsForecasting
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This app is (1) an ETL tool for ancillary data (2) a light-weight MLOps tool for the modeling of energy efficiency and electrification measure-level impact shapes, primarily as it relates to system planning assumptions and (3) a user interface to ES&P's forecast aggregation engine as a supply-side resource.
12_outputs: Generation of EEDR shapes and forecasts
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2019
19_date_acq_dev_began: 8/1/2019
20_date_implemented: 12/1/2021
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: EnergyRight and Advanced Analytics databases (components originally from Enterprise Planning system marginal value analysis, system load forecast, TVA EE/BE technical resource manual).
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: CESASolarModel
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This app allows internal TVA users to generate sample bills for directly served and BCD-rate customers based on active wholesale rates, historical load, and expected shape/magnitude of new behind-the-meter solar arrays.
12_outputs: Sample bills based on input data
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/3/2021
19_date_acq_dev_began: 5/19/2021
20_date_implemented: 1/7/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Power Billing (PBS), Electricity Sales Statistics (ESS), EIA Henry Hub gas prices and weather, Enterprise Planning solar shape library, PV Watts solar shape generator.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: CESAFCAAllocator
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This app is used by one of two co-calculators for the 3-class allocation of TVA's total monthly fuel cost (TMFC) to large general service customers, large manufacturing customers, and customers smaller than 5 MW.
12_outputs: Monthly fuel cost
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/31/2021
19_date_acq_dev_began: 3/31/2021
20_date_implemented: 1/25/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Electricity Sales Statistics (ESS) via CESA data warehouse (CESADB)
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: EnergyRightDERPortfolioEvaluation
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Commercial Energy Solutions
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This app is primarily a visualization and export tool for the outputs of ES&P's system planning processes through which Enterprise Planning can obtain aggregations of ES&P forecasts and ES&P can gain insight into evaluation methodologies. It also contains a connection to the ES&P forecast aggregation engine for reporting purposes and stochastic simulation tools.
12_outputs: Report of ES&Ps aggregated forecasts
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2019
20_date_implemented: 11/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: EnergyRight and Advanced Analytics databases (components originally from Enterprise Planning system marginal value analysis, system load forecast, TVA EE/BE technical resource manual).
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: R Shiny Web application
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Prism Models for MDM
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Monitoring Diagnostics Management
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: Use of clustering and nearest neighbor models to analyze analog signal data looking for variations in signals. Goal is to detect signal anomalies and provide alarms on deviations.
12_outputs: OMR - measure of how far current vector is from normal . Output includes an automated generated emails for human in the loop validation
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2013
19_date_acq_dev_began: 5/1/2013
20_date_implemented: 10/1/2013
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
28_iqa_compliance: Data quality and model quality checks
31_data_catalog: Yes
40_has_ato: Yes
41_system_name: Prism
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
56_monitor_postdeploy: Established Process of Machine Learning Operations: Alongside automated testing and drift detection, model re-training and re-deployments are supported by continuous integration pipelines that are managed by machine learning and data engineers on the platform, adapting work done by data science team into repeatable scripts for re-training and re-testing a model once deployed.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: Email
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Load Forecasting
3_agency: Tennessee Valley Authority
3_abr: TVA
4_bureau: Enterprise Planning
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: Predict energy and demand out to 30 years in order to support power supply planning
12_outputs: 30 year capacity and energy forecasts
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2010
19_date_acq_dev_began: 10/13/2010
20_date_implemented: 5/12/2011
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
28_iqa_compliance: Data quality and model quality checks
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Economic forecasts, EEDR forecasts, EV forecasts, commodity forecasts
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Itron system
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Yes
54_key_risks: Data quality
56_monitor_postdeploy: Established Process of Machine Learning Operations: Alongside automated testing and drift detection, model re-training and re-deployments are supported by continuous integration pipelines that are managed by machine learning and data engineers on the platform, adapting work done by data science team into repeatable scripts for re-training and re-testing a model once deployed.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - no demographic information used
63_stakeholder_consult: N/A - no demographic information used
65_appeal_process: Yes
67_opt_out: Yes