Solar Resource Areas created were produced using a k-means method that groups solar generating plants into discrete regions based on their latitude, longitude, and distance to the coast. After facilities were grouped, the outermost facilities in each region were connected to create the boundaries for each region. To include all facilities inside each polygon, a 5 km buffer was used. Plants that are farther from a concentration of other plants are not included in a region and are instead displayed as outlying facilities. Areas outside California included in the the regions are a result of the mapping process and are not representing actual solar generating facilities. Based on the Quarterly Fuel and Energy Report dataset, this map focuses on data from plants of at least 1 MW capacity (commercial scale) and excludes smaller plants.

Solar Footprints in California

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include:

GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)

Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023

Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows:

Rooftop Solar: Solar arrays located on rooftops of large buildings.

Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots.

Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints.

Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published.

This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints.

Metadata: (1) CBI Solar Footprints

Abstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:

https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advanced

The Solar Energy Industries Association’s “Project Location Map” which can be found here:

https://www.seia.org/map/majorprojectsmap.php

also assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:

https://www.eia.gov/maps/layer_info-m.cfm

There were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others

Solar PV Installations for Systems 1 MW and Smaller: 2023. Energy data and map are from the California Energy Commission CEC-1304B. Map depicts small solar photovoltaic capacity (with nameplate capacity of 1,000 kW or less). Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact John Hingtgen at (916) 510-9747 or john.Hingtgen@energy.ca.gov

The map depicts Solar Generation Regions of California, which are regions with concentrations of operating commercial solar plants. The regions were delineated using a k-means method that groups the plants into discreet regions based on their latitude, longitude and distance to the coast. After facilities were grouped, the outermost facilities in each region were connected to create the boundaries for each region. To include all facilities inside each polygon, a 5 km cartographic buffer was used. Plants that are farther from a concentration of other plants are not included. Areas outside California included in the regions are the result of the mapping process and do not represent solar generation facilities. Based on the Quarterly Fuel and Energy Report dataset, this map focuses on plants of commercial scale (at least 1 MW capacity). For more information please contact John Hingtgen at john.hingtgen@energy.ca .gov. October 2022

Map of 2019 Utility-Scale Solar Capacity by CountyThis map of California depicts the amount of utility scale solar generation capacity in each county (MW). This data is for 2019 and statewide totals are indicated.

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy

This map of California depicts the amount of solar energy produced in each county (gigawatt hours) as well as the capacity (MW) of each county's utility-scale resources. This data is for 2018 and statewide totals are indicated.

Energy data is collected for power plants that have a nameplate capacity of 1 MW or more. Counties that are gray either did not report data or had no utility-scale (commercial) solar capacity. Map and data originate from the California Energy Commission Quarterly Fuel and Energy Reports. Data is classified using the Jenk’s Natural Break’s method. Data is from 2020 and is current as of December 13, 2021. Projection: NAD 1983 California (Teale) Albers. For more information, please contact Rebecca Vail at (916) 477-0738 or John Hingtgen at (916) 510-9747.

Map of the concentrating solar power prospects for California. Data obtained from the National Renewable Energy Laboratory (NREL) in 2008.NOTE: The direct normal solar resource measurements shown are derived from 10km Perez data (2004), with modifications by NREL. Potentially sensitive lands, major urban areas, and water features have been excluded. Areas with resource < 6.75 kwh/m2/day, slope > 1% and minimum contiguous < 5 square kilometers were also excluded to identify those areas with the greatest potential for development.

Energy generation data and map are from the Quarterly Fuel and Energy Reporting Form CEC-1304B and the California Energy Commission. Map depicts distributed solar photovoltaic capacity (with nameplate capacity of 1,000 kW or less). Data is from December 2021 and is current as of August 18, 2022. Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact Rebecca Vail at (916) 477-0738 or John Hingtgen at (916) 510-9747.

Energy data and map are from the California Energy Commission CEC-1304B. Map depicts small solar photovoltaic capacity (with nameplate capacity of 1,000 kW or less). Data is from January 2023 and is current as of July 21,2023.

Solar PV Installations for Systems 1 MW and Smaller: 2024. Energy data and
map are from the California Energy Commission CEC-1304B.
Map depicts small solar photovoltaic capacity (with nameplate capacity of
1,000 kW or less). Projection: NAD 1983 (2011) California (Teale) Albers
(Meters). For more information, contact John Hingtgen at (916) 510-9747 or
john.hingtgen@energy.ca.gov.

Reporting requirements for power plants at least 1 MW are in accordance with 20 CA CCR 304 and 1385. Counties without pie symbols had no utility-scale (commercial) electric generation installed. Distributed renewable generation (e.g. rooftop solar) is not included. Map and data from the California Energy Commission. Energy production data is from the Quarter Fuel and Energy Report (QFER) and the Wind Performance Report System (WPRS) databases. Data is from 2018, and is current as of June 2019. Contact Dylan Kojimoto at (916) 651-0477 or John Hingtgen at (916) 657-4046 for questions.

Energy data is collected for power plants that have a nameplate capacity of 1MW or more. Counties that are gray had no utility-scale solar capacity. Dataoriginates from the California Energy Commission, and is classified using theJenk’s Natural Break’s method. Projection: NAD 1983 California (Teale) Albers.Map created by Rebecca Vail. Map and data are current as of November 3,2022. For more information, please contact John Hingtgen atJohn.hingtgen@energy.ca.gov.

This spatial vector dataset shows areas of identified high quality potential for Solar Photovoltaic (PV) development divided into large contiguous areas called "zones." This dataset contains zones for the Southern Africa Power Pool (SAPP) region. This is one of many products resulting from a study led by the International Renewable Energy Agency (IRENA) and the Lawrence Berkeley National Laboratory (LBNL) identifying wind and solar renewable energy zones for the Africa Clean Energy Corridor (ACEC). For each zone identified, multiple siting criteria were estimated, including the total and component levelized cost of electricity (LCOE), average capacity factor, distance to nearest grid infrastructure, distance to the nearest load center, average population density. For full documentation of the methods and descriptions of the attributes, please refer to the report and attribute information in the interactive PDF map. They can be found on the irena.org/Publications and mapre.lbl.gov websites. The information provided is meant to inform high-level policy debate (identification of opportunity areas for further prospection, preliminary assessment of technical potentials), or to perform market screening (cross referencing the resource information with policy information). It is suitable for decision-making activities, excluding financial commitments. By using these data, the user accepts IRENA and LBNL's Terms and Conditions shown here: IRENA: The designations employed and the presentation of materials herein do not imply the expression of any opinion whatsoever on the part of the International Renewable Energy Agency concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. While this publication promotes the adoption and use of renewable energy, the International Renewable Energy Agency does not endorse any particular project, product or service provider. LBNL: This document was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Using data collected in 2020, The City has created this solar potential dataset of all buildings within city limits. The data shows varying degrees of a roof’s solar exposure, on an annual basis, in generalized optimal conditions. The data model used to generate the map takes into account the shape of the terrain and the relative position of building rooftops and structures, existing infrastructure, and tree canopies. It doesn’t take into account weather conditions, such as cloudy days and precipitation that limit a roof’s direct solar exposure. It also does not reflect any new adjacent structures captured after 2020 that may obstruct another building’s solar exposure.

The Yield levels are measured as kWh\m2\day. Yield values are interpreted as: Low Yield (0 - 0.87); Low Moderate Yield (0.87 - 1.74); High Moderate Yield (1.74 - 2.61); High Yield (2.61 - 3.48)

The compressed file for download is 362 MB. The uncompressed files are 4.46 GB. Data is arranged in Townships, with 21 GeoTiff image files in total.

The Solar map data is intended for information purposes only and as a preliminary solar opportunity assessment tool. It is not intended to be used as a decision making source of information for solar panel installations.

Solar electrical generation data is reported for commercial power plants: those with a nameplate capacity of 1 MW or more. Counties in gray reported no generation in 2020. San Bernardino and Riverside county had solar thermal electric generation. Map and data from the California Energy Commission. Data is classified using the Jenk’s Natural Break’s method. Data is current as of November 22, 2021. Contact Rebecca Vail at (916)651- 0477 or John Hingtgen at (916) 510-9747 with questions.

This spatial vector dataset shows areas of identified high quality potential for Solar Photovoltaic (PV) development divided into large contiguous areas called "zones." This dataset contains zones for the Eastern Africa Power Pool (EAPP) region. This is one of many products resulting from a study led by the International Renewable Energy Agency (IRENA) and the Lawrence Berkeley National Laboratory (LBNL) identifying wind and solar renewable energy zones for the Africa Clean Energy Corridor (ACEC). For each zone identified, multiple siting criteria were estimated, including the total and component levelized cost of electricity (LCOE), average capacity factor, distance to nearest grid infrastructure, distance to the nearest load center, average population density.

For full documentation of the methods and descriptions of the attributes, please refer to the report and attribute information in the interactive PDF map. They can be found on the irena.org/Publications and mapre.lbl.gov

The information provided is meant to inform high-level policy debate (identification of opportunity areas for further prospection, preliminary assessment of technical potentials), or to perform market screening (cross referencing the resource information with policy information). It is suitable for decision-making activities, excluding financial commitments.

By using these data, the user accepts IRENA and LBNL's Terms and Conditions shown here:

IRENA: The designations employed and the presentation of materials herein do not imply the expression of any opinion whatsoever on the part of the International Renewable Energy Agency concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. While this publication promotes the adoption and use of renewable energy, the International Renewable Energy Agency does not endorse any particular project, product or service provider.

LBNL: This document was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

This statistic shows the leading metro areas in the U.S. with the most solar power jobs in 2017. As of that year, there were ****** solar energy jobs located in Los Angeles, California.

Utility Solar Generation and Capacity by Type and County Table: 2019