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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.

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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