PUDL Release Notes
Data Coverage Changes
Integration of 2020 data for all our core datasets (See #1255):
EPA IPM / NEEDS data has been removed from PUDL as we didn’t have the internal resources to maintain it, and it was no longer working. Apologies to @gschivley!
SQLite and Parquet Outputs
The ETL pipeline now outputs SQLite databases and Apache Parquet datasets directly, rather than generating tabular data packages. This is much faster and simpler, and also takes up less space on disk. Running the full ETL including all EPA CEMS data should now take around 2 hours if you have all the data downloaded.
pudl.load.parquetmodules contain this logic. The
pudl.load.metadatamodules have been removed along with other remaining datapackage infrastructure. See #1211
Many more tables now have natural primary keys explicitly specified within the database schema.
datapkg_to_sqlitescript has been removed and the
epacems_to_parquetscript can now be used to process the original EPA CEMS CSV data directly to Parquet using an existing PUDL database to source plant timezones. See #1176, #806.
Data types, specified value constraints, and the uniqueness / non-null constraints on primary keys are validated during insertion into the SQLite DB.
The PUDL ETL CLI
pudl.clinow has flags to toggle various constraint checks including
New Metadata System
With the deprecation of tabular data package outputs, we’ve adopted a more
modular metadata management system that uses Pydantic. This setup will allow us to easily
validate the metadata schema and export to a variety of formats to support data
distribution via Datasette and Intake catalogs, and automatic generation of data
dictionaries and documentation. See #806, #1271, #1272 and the
subpackage. Many thanks to @ezwelty for most of this work.
ETL Settings File Format Changed
We are also using Pydantic to parse and
validate the YAML settings files that tell PUDL what data to include in an ETL run. If
you have any old settings files of your own lying around they’ll need to be updated.
Examples of the new format will be deployed to your system if you re-run the
pudl_setup script. Or you can make a copy of the
etl_fast.yml files that are stored under
edit them to reflect your needs.
Database Schema Changes
With the direct database output and the new metadata system, it’s much eaiser for us to create foreign key relationships automatically. Updates that are in progress to the database normalization and entity resolution process also benefit from using natural primary keys when possible. As a result we’ve made some changes to the PUDL database schema, which will probably affect some users.
We have split out a new generation_fuel_nuclear_eia923 table from the existing generation_fuel_eia923 table, as nuclear generation and fuel consumption are reported at the generation unit level, rather than the plant level, requiring a different natural primary key. See #851, #1296, #1325.
Implementing a natural primary key for the boiler_fuel_eia923 table required the aggregation of a small number of records that didn’t have well-defined
prime_mover_codevalues. See #852, #1306, #1311.
We repaired, aggregated, or dropped a small number of records in the generation_eia923 (See #1208, #1248) and ownership_eia860 (See #1207, #1258) tables due to null values in their primary key columns.
Many new foreign key constraints are being enforced between the EIA data tables, entity tables, and coding tables. See #1196.
Fuel types and energy sources reported to EIA are now defined in / constrained by the static energy_sources_eia table.
The columns that indicate the mode of transport for various fuels now contain short codes rather than longer labels, and are defined in / constrained by the static fuel_transportation_modes_eia table.
In the simplified FERC 1 fuel type categories, we’re now using other instead of unknown.
Several columns have been renamed to harmonize meanings between different tables and datasets, including:
In generation_fuel_eia923 and boiler_fuel_eia923 the
fuel_type_codecolumns have been replaced with
energy_source_code, which appears in various forms in generators_eia860 and fuel_receipts_costs_eia923.
sector_name` and `sector_id` are now ``sector_name_eiaand
mine_type(a human readable label, not a code).
Added a deployed console script for running the state-level hourly electricity demand allocation, using FERC 714 and EIA 861 data, simply called
state_demandand implemented in
pudl.analysis.state_demand. This script existed in the v0.4.0 release, but was not deployed on the user’s system.
SQLAlchemy 1.4.x: Addressed all deprecation warnings associated with API changes coming in SQLAlchemy 2.0, and bumped current requirement to 1.4.x
Pandas 1.3.x: Addressed many data type issues resulting from changes in how Pandas preserves and propagates ExtensionArray / nullable data types.
PyArrow v5.0.0 Updated to the most recent version
PyGEOS v0.10.x Updated to the most recent version
contextily has been removed, since we only used it optionally for making a single visualization and it has substantial dependencies itself.
goodtables-pandas-py has been removed since we’re no longer producing or validating datapackages.
SQLite 3.32.0 The type checks that we’ve implemented currently only work with SQLite version 3.32.0 or later, as we discovered in debugging build failures on PR #1228. Unfortunately Ubuntu 20.04 LTS shipped with SQLite 3.31.1. Using
condato manage your Python environment avoids this issue.
This is a ridiculously large update including more than a year and a half’s worth of work.
New Data Coverage
Documentation & Data Accessibility
We’ve updated and (hopefully) clarified the documentation, and no longer expect most users to perform the data processing on their own. Instead, we are offering several methods of directly accessing already processed data:
Processed data archives on Zenodo that include a Docker container preserving the required software environment for working with the data.
Users who still want to run the ETL themselves will need to set up the set up the PUDL development environment
Data Cleaning & Integration
We now inject placeholder utilities in the cloned FERC Form 1 database when respondent IDs appear in the data tables, but not in the respondent table. This addresses a bunch of unsatisfied foreign key constraints in the original databases published by FERC.
We’re doing much more software testing and data validation, and so hopefully we’re catching more issues early on.
Hourly Electricity Demand and Historical Utility Territories
With support from GridLab and in collaboration with researchers at Berkeley’s Center for Environmental Public Policy, we did a bunch of work on spatially attributing hourly historical electricity demand. This work was largely done by @ezwelty and @yashkumar1803 and included:
Semi-programmatic compilation of historical utility and balancing authority service territory geometries based on the counties associated with utilities, and the utilities associated with balancing authorities in the EIA 861 (2001-2019). See e.g. #670 but also many others.
A method for spatially allocating hourly electricity demand from FERC 714 to US states based on the overlapping historical utility service territories described above. See #741
A fast timeseries outlier detection routine for cleaning up the FERC 714 hourly data using correlations between the time series reported by all of the different entities. See #871
Net Generation and Fuel Consumption for All Generators
We have developed an experimental methodology to produce net generation and fuel consumption for all generators. The process has known issues and is being actively developed. See #989
Net electricity generation and fuel consumption are reported in multiple ways in the EIA 923. The generation_fuel_eia923 table reports both generation and fuel consumption, and breaks them down by plant, prime mover, and fuel. In parallel, the generation_eia923 table reports generation by generator, and the boiler_fuel_eia923 table reports fuel consumption by boiler.
The generation_fuel_eia923 table is more complete, but the generation_eia923 + boiler_fuel_eia923 tables are more granular. The generation_eia923 table includes only ~55% of the total MWhs reported in the generation_fuel_eia923 table.
pudl.analysis.allocate_net_gen module estimates the net electricity
generation and fuel consumption attributable to individual generators based on
the more expansive reporting of the data in the generation_fuel_eia923
Data Management and Archiving
We now use a series of web scrapers to collect snapshots of the raw input data that is processed by PUDL. These original data are archived as Frictionless Data Packages on Zenodo, so that they can be accessed reproducibly and programmatically via a REST API. This addresses the problems we were having with the v0.3.x releases, in which the original data on the agency websites was liable to be modified long after its “final” release, rendering it incompatible with our software. These scrapers and the Zenodo archiving scripts can be found in our pudl-scrapers and pudl-zenodo-storage repositories. The archives themselves can be found within the Catalyst Cooperative community on Zenodo
There’s an experimental caching system that allows these Zenodo archives to work as long-term “cold storage” for citation and reproducibility, with cloud object storage acting as a much faster way to access the same data for day to day non-local use, implemented by @rousik
We’ve decided to shift to producing a combination of relational databases (SQLite files) and columnar data stores (Apache Parquet files) as the primary outputs of PUDL. Tabular Data Packages didn’t end up serving either database or spreadsheet users very well. The CSV file were often too large to access via spreadsheets, and users missed out on the relationships between data tables. Needing to separately load the data packages into SQLite and Parquet was a hassle and generated a lot of overly complicated and fragile code.
The EIA 861 and FERC 714 data are not yet integrated into the SQLite database outputs, because we need to overhaul our entity resolution process to accommodate them in the database structure. That work is ongoing, see #639
The EIA 860 and EIA 923 data don’t cover exactly the same rage of years. EIA 860 only goes back to 2004, while EIA 923 goes back to 2001. This is because the pre-2004 EIA 860 data is stored in the DBF file format, and we need to update our extraction code to deal with the different format. This means some analyses that require both EIA 860 and EIA 923 data (like the calculation of heat rates) can only be performed as far back as 2004 at the moment. See #848
There are 387 EIA utilities and 228 EIA palnts which appear in the EIA 923, but which haven’t yet been assigned PUDL IDs and associated with the corresponding utilities and plants reported in the FERC Form 1. These entities show up in the 2001-2008 EIA 923 data that was just integrated. These older plants and utilities can’t yet be used in conjuction with FERC data. When the EIA 860 data for 2001-2003 has been integrated, we will finish this manual ID assignment process. See #848, #1069
52 of the algorithmically assigned
plant_id_ferc1values found in the
plants_steam_ferc1table are currently associated with more than one
plant_id_pudlvalue (99 PUDL plant IDs are involved), indicating either that the algorithm is making poor assignments, or that the manually assigned
plant_id_pudlvalues are incorrect. This is out of several thousand distinct
plant_id_ferc1values. See #954
The county FIPS codes associated with coal mines reported in the Fuel Receipts and Costs table are being treated inconsistently in terms of their data types, especially in the output functions, so they are currently being output as floating point numbers that have been cast to strings, rather than zero-padded integers that are strings. See #1119
The primary changes in this release:
The 2009-2010 data for EIA 860 have been integrated, including updates to the data validation test cases.
Output tables are more uniform and less restrictive in what they include, no longer requiring PUDL Plant & Utility IDs in some tables. This release was used to compile v1.1.0 of the PUDL Data Release, which is archived at Zenodo under this DOI: https://doi.org/10.5281/zenodo.3672068
With this release, the EIA 860 & 923 data now (finally!) cover the same span of time. We do not anticipate integrating any older EIA 860 or 923 data at this time.
A couple of minor bugs were found in the preparation of the first PUDL data release:
No maximum version of Python was being specified in setup.py. PUDL currently only works on Python 3.7, not 3.8.
epacems_to_parquetconversion script was erroneously attempting to verify the availability of raw input data files, despite the fact that it now relies on the packaged post-ETL epacems data. Didn’t catch this before since it was always being run in a context where the original data was lying around… but that’s not the case when someone just downloads the released data packages and tries to load them.
This release is mostly about getting the infrastructure in place to do regular data releases via Zenodo, and updating ETL with 2018 data.
Added lots of data validation / quality assurance test cases in anticipation of archiving data. See the pudl.validate module for more details.
New data since v0.2.0 of PUDL:
EIA Form 860 for 2018
EIA Form 923 for 2018
FERC Form 1 for 1994-2003 and 2018 (select tables)
We removed the FERC Form 1 accumulated depreciation table from PUDL because it requires detailed row-mapping in order to be accurate across all the years. It and many other FERC tables will be integrated soon, using new row-mapping methods.
Lots of new plants and utilities integrated into the PUDL ID mapping process, for the earlier years (1994-2003). All years of FERC 1 data should be integrated for all future ferc1 tables.
Command line interfaces of some of the ETL scripts have changed, see their help messages for details.
This is the first release of PUDL to generate data packages as the canonical output, rather than loading data into a local PostgreSQL database. The data packages can then be used to generate a local SQLite database, without relying on any software being installed outside of the Python requirements specified for the catalyst.coop package.
This change will enable easier installation of PUDL, as well as archiving and bulk distribution of the data products in a platform independent format.
This is the only release of PUDL that will be made that makes use of PostgreSQL as the primary data product. It is provided for reference, in case there are users relying on this setup who need access to a well defined release.