buds-lab/energystar-plus-plus

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buds-lab/energystar-plus-plus

buds-lab

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Using Gradient Boosting Trees and Explainable ML for Commericial Building Benchmarking

the-building-data-genome-project

A collection of non-residential buildings for performance analysis and algorithm benchmarking

Jupyter Notebook

building-data-genome-project-2

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

Jupyter Notebook

ashrae-great-energy-predictor-3-solution-analysis

Analysis of top give winning solutions of the ASHRAE Great Energy Predictor III competition

Jupyter Notebook

python-for-building-analysts

Jupyter notebook tutorials to teach scripting to building performance analysis experts

Jupyter Notebook

building-prediction-benchmarking

An array of open source ML models applied to long-term hourly energy prediction for institutional buildings

Jupyter Notebook

simple-building

Simplified Building Simulation Engine

data-science-for-construction-edx-course-notebooks

Jupyter/Colab Notebooks for Data Science for Construction, Architecture and Engineering

Jupyter Notebook

google-trends-for-buildings

Data and Code for the Paper "Using Google Trends to Predict Building Energy"

Jupyter Notebook

temporal-features-for-nonres-buildings-library

Jupyter notebooks for the Energy and Buildings Publication

Jupyter Notebook

buds-lab.github.io

BUDS Lab Website

build2vec-thermal-comfort

code for Build2Vec 1.0 reproducibility

Jupyter Notebook

united-world-college-open-data

An IPython notebook analysis of the UWC Tampines commercial building dataset

Jupyter Notebook

LEAD-1st-solution

1st winning solution in Large-scale Energy Anomaly Detection (LEAD) competition

Jupyter Notebook

humans-as-a-sensor-for-buildings

Implementation of the Humans-as-a-Sensor for Buildings paper.

Jupyter Notebook

forensic-analysis-of-building-energy-data

Example Dataset from SimAUD 2015 Paper

energy-diffusion

Jupyter Notebook

building-data-directory

psychrometric-chart-makeover

Adding more dimension to the psychrometric chart

ComfortLearn

This repository is the official implementation of ComfortLearn: Enabling agent-based occupant-centric building controls

Jupyter Notebook

ashrae-great-energy-predictor-3-overview-analysis

Paper in Science and Technology for the Built Environment about the GEPIII Competition

Jupyter Notebook

aldiplusplus

This repository is the official implementation of ALDI++: Automatic and parameter-less discorddetection for daily load energy profiles

Jupyter Notebook

enerNOC-100-building-open-dataset-analysis

An IPython notebook overview of EnerNOC's open dataset

Jupyter Notebook

longitudinal-personal-thermal-comfort

Official repository for Dataset: Longitudinal personal thermal comfort preference data in the wild

Jupyter Notebook

comfortGAN

This repository is the official implementation of Balancing thermal comfort datasets: We GAN, but should we?

Jupyter Notebook

ccm

This repository is the official implementation of Cohort comfort models - Using occupants’ similarity to predict personal thermal preference with less data

Jupyter Notebook

day-filter

Automated daily pattern filtering of measured building performance data

Jupyter Notebook

elastic-buildings

Jupyter Notebook

jupyter-data-science-meetup

NUS Data Science Meetup - Data Science Workflow Tutorial in Jupyter

Jupyter Notebook

data-driven-greenmark

Dataset on Singapore's Green Mark Buildings

Jupyter Notebook

buildsys22-energy-forecasting-tutorial

Jupyter Notebook

ashrae-great-energy-predictor-3-error-analysis

Analysis of the Time Series Residuals of the Great Energy Predictor III competition

Jupyter Notebook

review-unsupervised-visualanalytics-for-buildings

A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings

Jupyter Notebook

generative-methods-for-human-comfort

Human comfort datasets are widely used for multiple scenarios in smart buildings. From thermal comfort prediction to personalized indoor environments, labelled subjective responses from participants in a experiment are required to feed different machine learning models. However, many of these dataset are small in samples per participants, number of participants, or suffer from a class-imbalanced of its subjective responses. In this work we explore the use of Generative Adversarial Networks to generate synthetic samples to be used in combination with real ones for data-driven applications in the built environment.

Jupyter Notebook

ema-for-occupant-wellness-and-privacy

Cozie deployment for Indoor Air 2022 Paper on Occupant Wellness and Privacy

project-iris-dataset

Jupyter Notebook

twenty-years-of-bldgsim-textmining

Text mining the email repository of the BLDG-SIM list serv

Jupyter Notebook

island-of-misfit-buildings

Detecting mixed-use or primary-space-use outliers using load shape clustering

Jupyter Notebook

style-guide

The budslab style guide

spacematch-paper

spacematch paper repo

learning-trail-scroller-demo

Scrollytelling Demo for Learning Trail Stations

iob

Internet of Buildings Center

nus-pf1103-digital-construction

Data for NUS PF1103 Digital Construction Module

recommender-sys-for-buildings-textmining-review

Jupyter Notebook

abm-demo

OpenAI workshop for occupant-centric applications

Jupyter Notebook

cozie-examples

Repository of example scripts to interface with the cozie app

Filling-time-series-gaps-using-image-techniques

Jupyter Notebook