New to OpenStreetMaps - Power Layer - Educational Purposes - Any tips on style format?

• SOFTX-D-22-00275

  1 2 10.0

  Convert OpenStreetMap grid data

  

GridTool Current: - https://github.com/ElsevierSoftwareX/SOFTX-D-22-00275GridTool Old - https://github.com/IEE-TUGraz/GridToolGridTool Research Paper: https://www.sciencedirect.com/science/article/pii/S2352711023000109Other:Grid Finder- gridfinder uses night-time lights imagery to as an indicator of settlements/towns with grid electricity access. Then a minimum spanning tree is calculated for these connect points, using a many-to-many variant Dijkstra algorithm and using existing road networks as a cost function. Adapted from this work from Facebook. Currently gridfinder only uses road networks, but it would be trivial to add other cost parameters such as slope or terrain.https://gridfinder.rdrn.me/https://github.com/carderne/gridfinder

• GridTool

  1 4 0.0 MATLAB

  Convert OpenStreetMap grid data

GridTool Current: - https://github.com/ElsevierSoftwareX/SOFTX-D-22-00275GridTool Old - https://github.com/IEE-TUGraz/GridToolGridTool Research Paper: https://www.sciencedirect.com/science/article/pii/S2352711023000109Other:Grid Finder- gridfinder uses night-time lights imagery to as an indicator of settlements/towns with grid electricity access. Then a minimum spanning tree is calculated for these connect points, using a many-to-many variant Dijkstra algorithm and using existing road networks as a cost function. Adapted from this work from Facebook. Currently gridfinder only uses road networks, but it would be trivial to add other cost parameters such as slope or terrain.https://gridfinder.rdrn.me/https://github.com/carderne/gridfinder

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• gridfinder

  1 58 7.0 Python

  Algorithm for guessing MV grid location based on NTL (by carderne)

GridTool Current: - https://github.com/ElsevierSoftwareX/SOFTX-D-22-00275GridTool Old - https://github.com/IEE-TUGraz/GridToolGridTool Research Paper: https://www.sciencedirect.com/science/article/pii/S2352711023000109Other:Grid Finder- gridfinder uses night-time lights imagery to as an indicator of settlements/towns with grid electricity access. Then a minimum spanning tree is calculated for these connect points, using a many-to-many variant Dijkstra algorithm and using existing road networks as a cost function. Adapted from this work from Facebook. Currently gridfinder only uses road networks, but it would be trivial to add other cost parameters such as slope or terrain.https://gridfinder.rdrn.me/https://github.com/carderne/gridfinder

• GridKit

  1 72 10.0 PLpgSQL

  GridKit is an power grid extraction toolkit

GridKit - https://github.com/bdw/GridKitGridKit uses spatial and topological analysis to transform map objects from OpenStreetMap into a network model of the electric power system. It has been developed in the context of the SciGRID project at the Next Energy research institute, to investigate the possibility of 'heuristic' analysis to augment the route-based analysis used in SciGRID. This has been implemented as a series of scripts for the PostgreSQL database using the PostGIS spatial extensions.

• transnet

  1 11 10.0 Python

  Transmission System Network Inference

  

Transnet - https://github.com/OpenGridMap/transnetThe Transnet project consists of a set of Python and Matlab scripts for the automatic inference of high voltage power (transmission) grids based on crowdsourced OpenStreetMap (OSM) data. Transnet yields two different models, a Common Information Model (CIM) model and a Matlab Simulink model. The latter can be used to perform load flow analysis. This manual guides you to the Transnet setup and gives several usage examples.

• GridMapping

  1 13 3.8 Jupyter Notebook

GridMapping: https://github.com/wangzhecheng/GridMappinghttps://www.nature.com/articles/s41467-023-39647-3Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.

• earth-osm

  1 18 8.2 Python

  Python tool to extract large-amounts of OpenStreetMap data

  

earth-osm - https://github.com/pypsa-meets-earth/earth-osmearth-osm is a python package that provides an end-to-end solution to extract & standardize power infrastructure data from OpenStreetmap (OSM).

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

Suggest a related project