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open-sustainable-technology - Listing of worldwide open technology projects preserving a stable climate, energy supply and vital natural resources

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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.

scaphandre - ⚡ Electrical power consumption metrology agent

  •    Rust

Scaphandre [skafɑ̃dʁ] is a metrology agent dedicated to electrical power consumption metrics. The goal of the project is to permit to any company or individual to measure the power consumption of its tech services and get this data in a convenient form, sending it through any monitoring or data analysis toolchain. Scaphandre means heavy diving suit in 🇫🇷. It comes from the idea that tech related services often don't track their power consumption and thus don't expose it to their clients. Most of the time the reason is a presumed bad ROI. Scaphandre makes, for tech providers and tech users, easier and cheaper to go under the surface to bring back the desired power consumption metrics, take better sustainability focused decisions, and then show the metrics to their clients to allow them to do the same.

edimax-smartplug - Unofficial Edimax Smartplug Libary. Control SP-1101W and SP-2101W from Node.js.

  •    Javascript

If you have installed the new version and wish to downgrade you can use the downgrade guide provided as part of the project. Note: Using the global broadcast address on Windows may yield unexpected results. On Windows, global broadcast packets will only be routed via the first network adapter which may cause problems with multi-homed setups and virtual network adapters. If you want to use a broadcast address though, use a network-specific address, e.g. for 192.168.0.1/24 use 192.168.0.255.

pyJoules - A Python library to capture the energy consumption of code snippets

  •    Python

pyJoules uses the Intel "Running Average Power Limit" (RAPL) technology that estimates power consumption of the CPU, ram and integrated GPU. This technology is available on Intel CPU since the Sandy Bridge generation(2010). PyJoule use hardware measurement tools (intel RAPL, nvidia GPU tools, ...) to measure device energy consumption. Theses tools have a mesasurement frequency that depend of the device. Thus, you can't use Pyjoule to measure energy consumption during a period shorter than the device energy measurement frequency. Pyjoule will return null values if the measurement period is to short.




pyRAPL - a library to measure the python energy consumption of python code

  •    Python

pyRAPL is a software toolkit to measure the energy footprint of a host machine along the execution of a piece of Python code. pyRAPL uses the Intel "Running Average Power Limit" (RAPL) technology that estimates power consumption of a CPU. This technology is available on Intel CPU since the Sandy Bridge generation.

Smart-Energy-Monitor - 🔌 Load Monitoring and Energy Disaggregation on a RasPi

  •    Python

The goal of the Smart Energy Monitor is to accurately predict the monthly electricity bill of the household using minimum hardware & by acquiring electrical data at a single location (instead of individual sensors per appliance). We do this by analyzing the current & power signatures of all the active devices and pass this information through a Naive Bayes classifier which helps us obtain the Active devices which can further be used to calculate the number of units consumed by individual load appliances. where N is the number of appliances and K is the number of appliance states. Since the complexity of CA is exponential to the number of appliances, the approach is only computationally tractable for a small number of modelled appliances. Hence, we chose to use a Naive Bayes classifier to determine the active appliances. A toy demonstration example is used to visualize how the number of computations increases as the number of appliances increases.

teb - 🏘️ The Town Energy Balance (TEB) model software and platform

  •    Fortran

This enhanced software and platform for TEB (Town Energy Balance; Masson, 2000 and subsequent papers), is intended to help scientists and practitioners wishing to use the TEB model in their research as a standalone software application or as a library (e.g. WRF-TEB) to calculate the urban surface energy balance at neighborhood scale assuming a simplified canyon geometry. By default, we set the real type to an 8 byte wide. This behavior is controlled by the optional USE_REAL8 flag (default ON).

ashrae-great-energy-predictor-3-solution-analysis - Analysis of top give winning solutions of the ASHRAE Great Energy Predictor III competition

  •    Jupyter

This repository contains the code and documentation of top-5 winning solutions from the ASHRAE - Great Energy Predictor III cometition that was held in late 2019 on the Kaggle platform. It also contains comparative analysis of these solutions with respect to their characteristics such as workflow, computation time, and score distributation with respect to meter type, site, and primary space usage, etc. In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. The competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.


building-data-genome-project-2 - Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition

  •    Jupyter

The GEPIII sub-set includes hourly data from 2,380 meters from 1,449 buildings that were used in a machine learning competition for long-term prediction with an application to measurement and verification in the building energy analysis domain. This data set can be used to benchmark various statistical learning algorithms and other data science techniques. It can also be used simply as a teaching or learning tool to practice dealing with measured performance data from large numbers of non-residential buildings. The charts below illustrate the breakdown of the buildings according to primary use category and subcategory, industry and subindustry, timezone and meter type. We recommend you download the Anaconda Python Distribution and use Jupyter to get an understanding of the data.






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