Displaying 1 to 6 from 6 results

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.

modelica-buildings - Modelica Buildings library

  •    Modelica

This is the development site for the Modelica Buildings library and its user guide. Stable releases including all previous releases are available from the main project site at http://simulationresearch.lbl.gov/modelica.

eemeter - An open source python package for implementing and developing standard methods for calculating normalized metered energy consumption and avoided energy use

  •    Python

EEmeter — an open source toolkit for implementing and developing standard methods for calculating normalized metered energy consumption (NMEC) and avoided energy use. At time of writing (Sept 2018), the OpenEEmeter, as implemented in the eemeter package and sibling eeweather package, contains the most complete open source implementation of the CalTRACK Methods, which specify a family of ways to calculate and aggregate estimates avoided energy use at a single meter particularly suitable for use in pay-for-performance (P4P) programs.

NYCBuildingEnergyUse - Creating Regression Models Of Building Emissions On Google Cloud

  •    Jupyter

In indentifying outliers I will cover both visual inspection as well a machine learning method called Isolation Forests. Since I will completing this project over multiple days and using Google Cloud, I will go over the basics of using BigQuery for storing the datasets so I won't have to start all over again each time I work on it. At the end of this blogpost I will summarize the findings, and give some specific recommendations to reduce mulitfamily and office building energy usage. In this second post I cover imputations techniques for missing data using Scikit-Learn's impute module using both point estimates (i.e. mean, median) using the SimpleImputer class as well as more complicated regression models (i.e. KNN) using the IterativeImputer class. The later requires that the features in the model are correlated. This is indeed the case for our dataset and in our particular case we also need to transform the feautres in order to discern a more meaningful and predictive relationship between them. As we will see, the transformation of the features also gives us much better results for imputing missing values.




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.

the-building-data-genome-project - A collection of non-residential buildings for performance analysis and algorithm benchmarking

  •    Jupyter

It is an open data set from 507 non-residential buildings that includes hourly whole building electrical meter data for one year. Each of the buildings has meta data such as or area, weather, and primary use type. 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 location, building industry, sub-industry, and primary use type. Clayton Miller, Forrest Meggers, The Building Data Genome Project: An open, public data set from non-residential building electrical meters, Energy Procedia, Volume 122, September 2017, Pages 439-444, ISSN 1876-6102, https://doi.org/10.1016/j.egypro.2017.07.400.






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