DEER Load Shapes
Load shapes can be defined as the hourly pattern of loads for a given set of variables including equipment, operating characteristics, and other factors such as weather and demographics. Currently, the load shapes for the deemed measures are developed through the Database for Energy Efficient Resources (DEER) simulation models. To keep the process transparent and flexible while ensuring the measure load shapes are accurate, the CPUC has developed four sets of parameters to standardize how hourly load shapes are characterized for measures across the four roadmaps. These generalized load shape (GLS) parameters can then be used to generate an 8760 load shape for any calendar year. These parameters describe the following:
- Monthly factors for percent allocation by month (primarily applicable to seasonal loads)
- Monthly ratios of average weekend day to average weekday (most applicable to non-residential)
- Monthly ratios of peak day to average weekday (primarily weather-related)
- Monthly per-unit hourly load shapes by day type (peak day, weekday and weekend day)
(The load shapes in this context are developed based on measure case consumption load profiles. )
The CPUC has prepared the following reference files as shown:
| Content | Description | Size | Updated |
| 2022 Load Shape Webinar Slide Deck | A CPUC webinar focused on the load shape tools (8760 to/from GLSP, load shape viewer) | 2.6 MB | 2022-2-18 |
| 2022 Load Shape Webinar Video Recording | A recording of the 2022 webinar. | 165 MB | 2022-2-18 |
| Python Code |
This zip file contains Python code developed to transform calendar-year specific 8760 load shapes to generalized load shape parameters–and back–is available for download. Companion Jupyter notebooks containing examples (conversion to- and from- 8760 load shapes) are also provided in the following path: DevOps_lopeshapes-tools_2022\loadshape-tools\lopeshape\templates\lopeshapes\2022_LoadshapeTools_8760_to_ls_sql_demo.ipynb DevOps_lopeshapes-tools_2022\loadshape-tools\lopeshape\templates\lopeshapes\2022_LoadshapeTools_ls_sql_to_8760_demo.ipynb |
1014 KB | 2022-2-17 |
| Loadshape-Measure Package Guidance Memo | A CPUC memo describing how 2023 measure packages shall be updated based on the new LoadShapeElec_2022 and LoadShapeGas_2022 tables in DEER. | 580 KB | 2021-12-7 |
| Methodology Memo | A memo describing the generalized load shape (GLS) parameter determination methodology | 1730 KB | 2020-7-29 |
| DEER Load Shape Generalized Factor Converter | This zip file contains an Excel workbook, LOADLIBA-8760-v14-CPUC-DEER.xlsx, to enable users to convert from 8760s to GLS parameters using a single existing load shape (8760). This is useful only for viewing exactly how the transformation is performed; the Python code is to be used to transform a batch of 8760s to GLSPs or vice versa. | 16.6 MB | 2020-7-29 |
| 2020 Load Shape Webinar Slide Deck | A CPUC webinar describing the generalized load shape parameter (GLSP) methodology. | 6.4 MB | 2020-8-5 |
| DEER2020/ 2023 Load Shapes, Part 1 of 2 |
This zip file contains the measure-case energy consumption load shapes and GLS parameters using both CZ2010 and CZ2022 weather data for the following measures (for the 1985 building vintage):
|
68.9 MB | 2020-8-5 |
| DEER2020/ 2023 Load Shapes, Part 2 of 2 |
This zip file contains the measure-case energy consumption load shapes and GLS parameters using both CZ2010 and CZ2022 weather data for the following MeasureIDs (for the 1985 building vintage):
|
78.9 MB | 2020-8-5 |
| MASControl3© Runs for 1985 BldgVint | This zip file contains the 8,760 data used to produce the compressed 24-hour format load shape examples were generated from current DEER measures by using MASControl3© | 386.9 MB | 2020-8-28 |