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open-sustainable-technology - Listing of worldwide open technology projects preserving a stable climate, energy supply and vital natural resources
A curated list of open technology projects to sustain a stable climate, energy supply, and vital natural resources. Our ambition is to list all sustainable, open and actively maintained sustainable technology projects worldwide. Your contribution is necessary to keep this list alive, increase the quality and to expand it. Read more about its origin and how you can participate in the contribution guide, community chat, presentation slides and related blog post. Please contact us to give feedback, hints and ideas for OpenSustain.tech or create an issue.
awesome energy sustainability battery climate ocean photovoltaic awesome-list wind-turbine carbon-emissions mobility climate-change energy-consumption electric-vehicles soil energy-storage renewable-energy carbon-footprint clean-energy environmental-technologypvlib-python - A set of documented functions for simulating the performance of photovoltaic energy systems
pvlib python is a community supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. pvlib python was originally ported from the PVLIB MATLAB toolbox developed at Sandia National Laboratories and it implements many of the models and methods developed at the Labs. More information on Sandia Labs PV performance modeling programs can be found at https://pvpmc.sandia.gov/. We collaborate with the PVLIB MATLAB project, but operate independently of it. Full documentation can be found at readthedocs.
photovoltaic solar-energy renewable-energy renewableseuro-calliope - A model of the European power system built using Calliope.
A model of the European electricity system built using Calliope. This repository contains the workflow routines that automatically build models from source data. Alternatively to building models yourself, you can use pre-built models that run out-of-the-box. You can find a more detailed description of the first application in a scientific article in Joule.
energy research conda europe renewable-energy calliope-modelsantaresViz - ANTARES Visualizations
antaresViz is the package to visualize the results of your Antares simulations that you have imported in the R session with package antaresRead. It provides some functions that generate interactive visualisations. Moreover, by default, these functions launch a shiny widget that provides some controls to dynamically choose what data is displayed in the graphics. antaresViz provides a plot method for tables generated with antaresRead. This method is for visualizing a single variable in different formats (times series, barplot, monotone, distribution and cumulative distribution). For instance, the following code displays the distribution of marginal price in different areas.
r energy simulation shiny optimization leaflet plotly linear-programming electric monte-carlo-simulation shiny-apps dygraphs rte stochastic-simulation-algorithm renewable-energy adequacy manipulatewidge bilan previsionnel tyndppvfactors - :sunny: Open-source view-factor model for diffuse shading and bifacial PV modeling
pvfactors is a tool used by PV professionals to calculate the irradiance incident on surfaces of a photovoltaic array. It relies on the use of 2D geometries and view factors integrated mathematically into systems of equations to account for reflections between all of the surfaces. pvfactors was originally ported from the SunPower developed 'vf_model' package, which was introduced at the IEEE PV Specialist Conference 44 2017 (see [1] and link to paper).
solar-energy renewable-energy bifacialbifacial_radiance - Toolkit for working with RADIANCE for the ray-trace modeling of Bifacial Photovoltaics
bifacial_radiance contains a series of Python wrapper functions to make working with RADIANCE easier, particularly for the PV researcher interested in bifacial PV performance. For more information, check out our documentation, Tutorials in the form of Jupyter Notebooks, or reffer to our Wiki and Issues page. https://youtu.be/4A9GocfHKyM This video shows how to install the bifacial_radiance software and all associated software needed. More info on the Wiki. Instructions are also shown below.
gui radiance renewable-energy renewables photovoltaics bifacialREopt_Lite_API - The model for the REopt Lite API, which is used as the back-end for the REopt Lite Webtool (reopt
The REopt™ Lite model in this repository is a free, open-source, development version of the REopt™ Lite API. A production version of the REopt™ Lite API lies behind the REopt™ Lite Webtool. REopt Lite offers a subset of features from NREL's more comprehensive REopt model. Both models provide concurrent, multiple technology integration and optimization capabilities to help organizations meet their cost savings and energy performance goals. Formulated as a mixed integer linear program, the REopt models recommend an optimally sized mix of renewable energy, conventional generation, and energy storage technologies; estimates the net present value of implementing those technologies; and provides a dispatch strategy for operating the technology mix at maximum economic efficiency. A comparison of the REopt and REopt Lite models is provided here.
optimization nrel renewable-energy reopt-lite-apiNYISOToolkit - Access data, statistics, and visualizations for New York's electricity grid.
A package for accessing power system data (NYISOData), generating statistics (NYISOStat), and creating visualizations (NYISOVis) from the New York Independent System Operator (NYISO). There are several visualizations currently supported - browse them on the NYISOToolkit Web App or in the nyisotoolkit/nyisovis/visualizations folder. The visualizations are focused on communicating New York's status toward achieving the power sector decarbonization goals outlined by the Climate Leadership and Community Protection Act (CLCPA).
visualization data machine-learning energy analysis ml kaggle datascience kaggle-competition electricity datasets renewable-energy kaggle-dataset nyiso newyork clean-energy decarbonization clcpapyGRETA - python Generator of REnewable Time series and mAps
python Generator of REnewable Time series and mAps: a tool that generates high-resolution potential maps and time series for user-defined regions within the globe.
csp gis wind high-resolution pv renewable-energy potentials renewable-timeseriesglaes - Geospatial Land Availability for Energy Systems
GLAES is a framework for conducting land eligibility analyses and is designed to easily incorporate disparate geospatial information from a variety of sources into a unified solution. Currently, the main purpose of GLAES is performing land eligibility (LE) analyses which, in short, are used to determine which areas within a region are deemed 'eligible' for some purpose (such as placing a wind turbine). Although initially intended to operate in the context of distributed renewable energy systems, such as onshore wind and open-field solar parks, the work flow of GLAES is applicable to any context where a constrained indication of land is desired. Except in the context of Europe, GLAES only provides a framework for conducting these types of analyses, and so the underlying data sources which are used will need to be provided. Fortunately, GLAES is built on top of the Geospatial Data Abstraction Library (GDAL) and so is capable of incorporating information from any geospatial dataset which GDAL can interpret; including common GIS formats such as .shp and .tif files. In this way, GLAES affords a high degree of flexibility such that very specific considerations, while still maintaining a consistent application method between studies. A number of precomputed (Prior) datasets which constitute the most commonly considered criteria used for LE analyses have been constructed for the European context. These datasets are formatted to be used directly with the GLAES framework and, in doing so, drastically reduce the time requirements, data management, and overall complexity of conducting these analyses. The Priors also have the added benefit of providing a common data source to all LE researchers, which further promotes consistency between independent LE evaluations. Most important, usage of these datasets is just as easy as applying exclusions from other geospatial datasources. Although the Prior datasets are not included when cloning this repository, they can be downloaded and installed using the process detailed in the "Installation" section.
energy geospatial renewable-energy geospatial-analysisWindTurbineClassification - My master's dissertation on wind turbine fault prediction using machine learning
This repository is an archive of files created for my master's dissertation, which was completed between May and August 2017 at Heriot-Watt University and Natural Power Consultants. This was also my very first Python project. Unsurprisingly, these files needed some formatting to improve readability, which I did after my graduation. I did not make any major changes to the code or document, so this repository still contains a bunch of standalone scripts and not a package. The current branch has the formatted code and dissertation files. The dissertation can be viewed here [PDF download]. The original submission can be accessed using this branch, or by downloading the v1.0.0 archive (also available on Zenodo).
energy jupyter-notebook renewable-energy wind-turbines
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